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国际自动化与计算杂志.英文版.1.Improved Exponential Stability Criteria for Uncertain Neutral System with Nonlinear Parameter PerturbationsFang Qiu，Ban-Tong Cui2.Robust Active Suspension Design Subject to Vehicle Inertial Parameter VariationsHai-Ping Du，Nong Zhang3.Delay-dependent Non-fragile H∞ Filtering for Uncertain Fuzzy Systems Based on Switching Fuzzy Model and Piecewise Lyapunov FunctionZhi-Le Xia，Jun-Min Li，Jiang-Rong Li4.Observer-based Adaptive Iterative Learning Control for Nonlinear Systems with Time-varying DelaysWei-Sheng Chen，Rui-Hong Li，Jing Li5.H∞ Output Feedback Control for Stochastic Systems with Mode-dependent Time-varying Delays and Markovian Jump ParametersXu-Dong Zhao，Qing-Shuang Zeng6.Delay and Its Time-derivative Dependent Robust Stability of Uncertain Neutral Systems with Saturating ActuatorsFatima El Haoussi，El Houssaine Tissir7.Parallel Fuzzy P+Fuzzy I+Fuzzy D Controller:Design and Performance EvaluationVineet Kumar，A.P.Mittal8.Observers for Descriptor Systems with Slope-restricted NonlinearitiesLin-Na Zhou，Chun-Yu Yang，Qing-Ling Zhang9.Parameterized Solution to a Class of Sylvester MatrixEquationsYu-Peng Qiao，Hong-Sheng Qi，Dai-Zhan Cheng10.Indirect Adaptive Fuzzy and Impulsive Control of Nonlinear SystemsHai-Bo Jiang11.Robust Fuzzy Tracking Control for Nonlinear Networked Control Systems with Integral Quadratic ConstraintsZhi-Sheng Chen，Yong He，Min Wu12.A Power-and Coverage-aware Clustering Scheme for Wireless Sensor NetworksLiang Xue，Xin-Ping Guan，Zhi-Xin Liu，Qing-Chao Zheng13.Guaranteed Cost Active Fault-tolerant Control of Networked Control System with Packet Dropout and Transmission DelayXiao-Yuan Luo，Mei-Jie Shang，Cai-Lian Chen，Xin-Ping Guanparison of Two Novel MRAS Based Strategies for Identifying Parameters in Permanent Magnet Synchronous MotorsKan Liu，Qiao Zhang，Zi-Qiang Zhu，Jing Zhang，An-Wen Shen，Paul Stewart15.Modeling and Analysis of Scheduling for Distributed Real-time Embedded SystemsHai-Tao Zhang，Gui-Fang Wu16.Passive Steganalysis Based on Higher Order Image Statistics of Curvelet TransformS.Geetha，Siva S.Sivatha Sindhu，N.Kamaraj17.Movement Invariants-based Algorithm for Medical Image Tilt CorrectionMei-Sen Pan，Jing-Tian Tang，Xiao-Li Yang18.Target Tracking and Obstacle Avoidance for Multi-agent SystemsJing Yan，Xin-Ping Guan，Fu-Xiao Tan19.Automatic Generation of Optimally Rigid Formations Using Decentralized MethodsRui Ren，Yu-Yan Zhang，Xiao-Yuan Luo，Shao-Bao Li20.Semi-blind Adaptive Beamforming for High-throughput Quadrature Amplitude Modulation SystemsSheng Chen，Wang Yao，Lajos Hanzo21.Throughput Analysis of IEEE 802.11 Multirate WLANs with Collision Aware Rate Adaptation AlgorithmDhanasekaran Senthilkumar，A. 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扩展二维环境中移动机器人多人体目标跟踪的开题报告1. 研究背景和意义目前，机器人技术的快速发展，为各种应用场景带来了更多可能性。
其中，移动机器人在人们的日常生活和工业生产中发挥着关键作用。
例如，移动机器人可以用于监视和控制一些复杂的工业生产线。
此外，机器人技术可以用于救援、地震等紧急事件的搜索、拯救和物资运输。
在这个过程中，机器人的自动感知和自我定位非常重要。
特别是，随着移动机器人数量的增加，机器人之间的通信和协作变得更为重要。
在这个背景下，开发出一种跨机器人的多人体目标跟踪方案，对于有效掌握整个环境和实现不同任务协作非常重要。
2. 研究内容和方案本研究将开发一种基于视觉感知和机器人协作的多人体目标跟踪方案。
具体来说，本方案将使用多目标检测算法进行人体目标检测，然后将检测到的目标位置信息传递给移动机器人。
机器人将使用定位和移动控制算法，将自己调整到最佳位置，从而更好地跟踪人体目标。
此外，为了提高跟踪精度，本方案将利用多机器人之间的相互通信，进行协同跟踪，以排除视线障碍和遮挡问题。
3. 研究计划和方法第一步，进行基于深度学习的多目标检测算法和人体姿态估计算法的研发和优化，以提高检测和姿态估计的准确性和速度。
第二步，设计机器人移动和定位算法，在保证满足目标跟踪的同时，保证移动机器人的能耗和行进路径最优。
第三步，设计机器人之间的通信协议，确保多机器人跟踪的协作具有较高的鲁棒性和效率。
第四步，开展动态实验，验证本方案的跟踪精度和效率。
4. 预期成果本研究将实现一种跨移动机器人的多人体目标跟踪方案，为自动感知和机器人协作的应用提供更好的实现途径。
预期成果包括：（1）基于深度学习的多目标检测和人体姿态估计算法的研发和优化；（2）移动机器人定位和移动控制算法及协同跟踪通信协议的设计；（3）多机器人跟踪实验的开展和数据分析，评估本方案的跟踪精度和效率。
5. 进度计划预计本研究将在四年内完成。
具体进度如下：（1）第1年：完成多目标检测和人体姿态估计算法的研发和优化，并进行初步实验。
前言：最近由于工作的关系，接触到了很多篇以前都没有听说过的经典文章，在感叹这些文章伟大的同时，也顿感自己视野的狭小。
想在网上找找计算机视觉界的经典文章汇总，一直没有找到。
失望之余，我决定自己总结一篇，希望对 CV领域的童鞋们有所帮助。
由于自
己的视野比较狭窄，肯定也有很多疏漏，权当抛砖引玉了
1990年之前
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1998年是图像处理和计算机视觉经典文章井喷的一年。
大概从这一年开始，开始有了新的趋势。
由于竞争的加剧，一些好的算法都先发在会议上了，先占个坑，等过一两年之后再扩展到会议上。
1999年
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世纪之交，各种综述都出来了
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2012年。
基于ORB特征的目标跟踪算法葛山峰;于莲芝;谢振【摘要】为提高基于特征点目标跟踪的速度,提出一种基于ORB特征的跟踪算法.通过对目标的ORB特征不断进行实时更新,去除过时的信息,以保证目标特征的准确与稳定,提高匹配准确度.实验结果表明,同基于SIFT和SURF特征的跟踪算法相比,在匹配表现相似的情况下,匹配速度大幅提高,基本满足了实时性的要求.【期刊名称】《电子科技》【年(卷),期】2017(030)002【总页数】4页(P98-100,104)【关键词】目标跟踪;ORB;特征匹配;实时性【作者】葛山峰;于莲芝;谢振【作者单位】上海理工大学光电信息与计算机工程学院,上海200093;上海理工大学光电信息与计算机工程学院,上海200093;上海理工大学光电信息与计算机工程学院,上海200093【正文语种】中文【中图分类】TN953;TP391.41目标跟踪算法是计算机视觉应用中关键技术，在医疗诊断、视频监控、军事视觉制导等领域有着非常重要的实用价值[1]。
跟踪技术主要分为4大类：基于模型的跟踪方法，基于主动轮廓的跟踪方法，基于运动估计的跟踪方法和基于特征点的跟踪方法[2]。
本文主要研究基于特征的目标跟踪算法，其步骤主要有两个步骤：特征提取和特征匹配。
2004年，David Lowe提出的尺度不变换SIFT算法[3]，2006年，Bay在SIFT的基础上提出基于积分图像的特征描述方法SURF算法[4]，2011年Ethan Rublee等人提出ORB特征算法[5]。
通过对比三个算法提出基于ORB特征的目标跟踪算法。
首先利用帧间差分法对运动目标进行分割，并对分割后的目标进行ORB特征变换，最后利用目标的特征值进行运动分析。
实验证明，在实时性方面ORB特征有着较大的优势，基本实现了跟踪系统的实时性。
运动目标检测常用方法：帧间差分法，背景减除法和光流法[6]。
帧间差分法能够快速得从每帧中检测出运动的目标，其最大的特点检测速度快，对整体光照不敏感，缺点是不能提取出对象的完整区域[7]。
基于相关滤波器的⽬标跟踪⽅法综述0引⾔视觉跟踪是计算机视觉中引⼈瞩⽬且快速发展的领域,主要⽤于获取运动⽬标的位置、姿态、轨迹等基本运动信息,是理解服务对象或对⽬标实施控制的前提和基础。
其涉及许多具有挑战性的研究热点并常和其他计算机视觉问题结合出现,如导航制导、事件检测、⾏为识别、视频监控、⾃动驾驶、移动机器⼈等[1-4]。
虽然跟踪⽅法取得了长⾜进展,但由于遮挡、⽬标的平⾯内/外旋转、快速运动、模糊、光照及变形等因素的存在使其仍然是⾮常具有挑战性的⼯作。
近年来,基于相关滤波器CF(Correlation Filter)的跟踪⽅法得到了极⼤关注[5-9]。
CF 最⼤的优点是计算效率⾼,这归结于其假设训练数据的循环结构,因为⽬标和候选区域能在频域进⾏表⽰并通过快速傅⾥叶变换(FFT)操作。
Bolme [6]等⾸次将CF 应⽤于跟踪提出MOSSE 算法,其利⽤FFT 的快速性使跟踪速度达到了600-700fps 。
瑞典林雪平⼤学的Martin Danelljan 在2016年ECCV 上提出的相关滤波器跟踪算法C -COT [7]取得了VOT2016竞赛冠军,2017年其提出的改进算法ECO [8]在取得⾮常好的精度和鲁棒性的同时,显著提⾼运算速度⾄C-COT 的6倍之多。
基于CF 的跟踪算法如此优秀,已然成为研究热点。
近年和相关滤波有关的论⽂层出不穷,很有必要对这些论⽂及相关滤波的发展等进⾏⼀个归纳和总结,以推动该⽅向的发展。
⽂献[9]虽已做过综述并取得了⼀定效果,但有两点不⾜:(1)过多介绍现有⼏种⽅法的具体细节,没有对更多⽂献进⾏对⽐分析;(2)缺乏对基于相关滤波器跟踪⽅法的分类对⽐分析。
基于此,本⽂的不同基⾦项⽬:陕西理⼯⼤学科研项⽬资助(SLGKY16-03)基于相关滤波器的⽬标跟踪⽅法综述?马晓虹1,尹向雷2(1.陕西理⼯⼤学电⼯电⼦实验中⼼,陕西汉中723000;2.陕西理⼯⼤学电⽓⼯程学院,陕西汉中723000)摘要:⽬标跟踪是计算机视觉中的重要组成部分,⼴泛应⽤于军事、医学、安防、⾃动驾驶等领域。
2023.12（卷二） Section AIt is important that scientists be seen as normal people asking and answering important questions. Good, sound science depends on _____（26）, experiments and reasoned methodologies. It requires a willingness to ask new questions and try new approaches. It requires one to ask risks and experience failures. But good science also requires _____（27）understanding, clear explanation and concise presentation.Our country needs more scientists who are willing to step out in the public _____（28）and offer their options on important matters. We need more scientists who can explain what they are doing in language that is _____（29）and understandable to the public. There of us who are not scientists should also be prepared to support public engagement by scientists, and to _____（30）scientific knowledge into our public communications.Too many people in this country, including some among our elected leadership, skill do not understand how science works or why robust, long-range investments in research vitally matter. In the 1960s, the United States _____（31）nearly 17% of discretionary（可酌情支配的）spending to research and development, _____（32）decades of economic growth. By 2023, the figure had fallen into the single _____（33）. This occurs at a time when other nations have made significant gains in their own research capabilities.At the University of California （UC）, we _____（34）ourselves not only on the quality of our research, but also on its contribution to improving our world. To _____（35）the development of science from the lab bench to the market place, UC is investing our money in our own good ideas.A．arena B．contextual C．convincing D．devoted E．digits F．hastenG. hypotheses H. impairing I. incorporate J. indefinite K. indulgeL. inertia M. pride N. reaping O. warrantAre We in an Innovation Lull？[A]Scan the highlights of this year's Consumer Electronics Show (CES), and you may get a slight feeling of having seen them before. Many of the coolest gadgets this year are the same as the coolest gadgets last year－or the year before, even. The booths are still exciting, and the demos are still just as crazy. It is still easy to be dazzled by the display of drones（无人机）,3D printers, virtual reality goggles（眼镜）and more "smart" devices than you could ever hope to catalog. Upon reflection, however, it is equally easy to feel like you have seen it all before. And it is hard not to think: Are we in an innovation lull（间歇期）?[B]In some ways, the answer is yes, For years, smartphones, television, tablets, laptops and desktops have made up a huge part of the market and driven innovation. But now these segments are looking at slower growth curves－or shrinking markets in some cases－as consumers are not as eager to spend money on new gadgets. Meanwhile, emerging technologies－the drones, 3D printers and smart-home devices of the world－now seem a bit too old to be called "the next big thing." [C]Basically the tech industry seems to be in an awkward period now, "There is not any one-hit wonder, and there will not be one for years to come," said Gary Shapiro, president and chief executive of the Consumer Technology Association (CTA). In this eyes, however, that doesn't necessarily mean that innovation has stopped. It has just grown up a little. "Many industries are going out of infancy and becoming adolescents," Shapiro said.[D]For instance, new technologies that are building upon existing technology have not found their footing well enough to appeal to a mass audience , because, in many cases, they need to work effectively with other devices to realize their full appeal, Take the evolution of the smart home, for example. Companies are pushing it hard but make it almost overwhelming even to dip a toe in the water for the averageconsumer, because there are so many compatibility issues to think about. No average person wants to figure out whether their favorite calendar software works with their fridge or whether their washing machine and tablet get along. Having to install a different app for each smart appliance in your home is annoying; it would be nicer if you could manage everything together. And while you may forgive your smartphone an occasional fault, you probably have less patience for error message from your door lock.[E]Companies are promoting their own standards, and the market has not had time to choose a winner yet as this is still very new. Companies that have long focused on hardware now have to think of ecosystems instead to give consumers practical solutions to their everyday problems. "The dialogue is changing from what is technologically possible to what is technologically meaningful." said economist Shawn DuBravac works for CTA－which puts on the show each year－and said that this shift to a search for solutions has been noticeable as he researched his predictions for 2023.[F]"So much of what CES has been about is the cool. It is about the flashiness and the gadgets," said John Curran, managing director of research at Accenture. "But over the last couple of years, and in this one in particular, we are starting to see companies shift from what is the largest screen size, the smallest from factor or the shiniest object and more into what all of these devices do that is practical in a consumer's life." Even the technology press conferences, which have been high-profile in the past and reached a level of drama and theatrics fitting for a Las Vegas stage, have a different bent to them. Rather than just dazzling with a high cool factor, there is a focus on the practical. Fitbit, for example, released its first smartwatch Monday, selling with a clear purpose－to improve your fitness－and promoting it as a "tool, not a toy." Not only that, it supports a number of platforms: Apple's iOS,Google's Android and Microsoft's Windows phone.[G]That seems to be what consumers are demanding, after all. Consumers are becoming increasingly bored with what companies have to offer: A survey of 28,000 consumers in 28 countries released by Accenture found consumers are not as excited about technology as they once were. For example, when asked whether they would buy a new smartphone this year, only 48 percent said yes－a six-point drop from 2023.[H]And when it comes to the hyper-connected super-smart world that technology firms are painting for us, it seems that consumers are growing more uneasy about handing over the massive amounts of consumer data needed to provide the personalized, customized solutions that companies need to improve their services. That could be another explanation for why companies seem to be strengthening their talk of the practicality of their devices.[I]Companies have already won part of the battle, having driven tech into every part of our lives, tracking our steps and our very heartbeats. Yet the persistent question of "Why do I need that?"－or, perhaps more tellingly, "Why do you need to know that?"－dogs the steps of many new ventures. Only 13 percent of respondents said that they were interested in buying a smartwatch in 2023, for example－an increase of just one percent from the previous year despite a year of high-profile launches. That is bad news for any firm that may hope that smartwatches can make up ground for maturing smartphone and tablet markets. And the survey found flat demand for fitness monitors, smart thermostats（恒温器）and connected home cameras, as well.[J]According to the survey, that lack of enthusiasm could stem from concern about privacy and security. Even among people who have bought connected devices of some kind, 37 percent future. A full 18 percent have even returned devices until theyfeel they can get safer guarantees against having their sensitive information backed. [K]That, too, explains the heavy Washington presence at this year's show, as these new technologies intrude upon heavily regulated areas. In addition to many senior officials from the Federal Trade and Federal Communications commissions, this year's list of policy makers also includes appearances from Transportations Secretary Anthony Foxx, to talk about smart cities, and Federal Aviation Administration Administrator Michael Huerta, to talk about drones.[L]Curran, the Accenture analyst, said that increased government interest in the show makes sense as technology becomes a larger part of our lives. "There is an incompatibility in the rate at which these are advancing relative to the way we're digesting it," he said, "Technology is becoming bigger and more aspirational, and penetrating almost every aspect of our lives. We have to understand and think about the implications, and balance these great innovations with the potential downsides they naturally carry with them."36.Consumers are often hesitant to try smart-home devices because they are worried about compatibility problems.37.This year's electronics show featured the presence of many officials from the federal government.38. The market demand for electronic devices is now either declining or not growing as fast as before.39.One analyst suggests it is necessary to accept both the positive and negative aspects of innovative products.40. The Consumer Electronics Show in recent years has begun to focus more on the practical value than the showiness of electronic devices.41. Fewer innovative products were found at this year's electronic products show.42.Consumers are becoming more worried about giving personal information totech companies to get customized products are services.43.The Consumer Technology Association is the sponsor of the annual Consumer Electronics Show.44. Many consumers wonder about the necessity of having their fitness monitored.45.The electronic industry is maturing even though no wonder products hit the market.Section C （一）The Paris climate agreement finalised in December last year heralded a new era for climate action. For the first time, the world's nations agreed to keep global warming well below 2℃.This is vital for climate-vulnerable nations. Fewer than 4% of countries are responsible for more than half of the world's greenhouse gas emissions. In a study published in Nature Scientific Reports, we reveal just how deep this injustice runs. Developed nations such as Australia, the United States, Canada, and European countries are essentially climate "free-riders": causing the majority of the problems through high greenhouse gas emissions, while incurring few of the costs such as climate change's impact on food and water, in other words, a few countries are benefiting enormously from the consumption of fossil fuels, while at the same time contributing disproportionately to the global burden of climate change.On the filp side, there are many "forced riders", who are suffering from the climate change impacts despite having scarcely contributed to the problem. Many of the world's most climate-vulnerable countries, the majority of which are African or small island states, produce a very small quantity of emissions. This is much like a non-smoker getting cancer from second-hand smoke, while the heavy smoker is fortunate enough to smoke in good health.The Paris agreement has been widely hailed as a positive step forward in addressing climate change for all, although the details on addressing "climate justice"can be best described as sketchy.The goal of keeping global temperature rise "well below" 2℃ is commendable but the emissions-reduction pledges submitted by countries leading up to the Paris talks are very unlikely to deliver on this.More than $100 billion in funding has been put on the table for supporting developing nations to reduce emissions. However, the agreement specifies that there is no formal distinction nations to reduce emissions. However, the agreement specifies that there is no formal distinction between developed and developing nations in their responsibility to cut emissions, effectively ignoring historical emissions. There is also very little detail on who will provide the funds or, importantly, who is responsible for their provision. Securing these funds, and establishing who is responsible for raising them will also be vital for the future of climate-vulnerable countries.The most climate-vulnerable countries in the world have contributed very little to creating the global disease from which they now suffer the most. There must urgently be a meaningful mobilisation of the policies outlined in the agreement if we are to achieve national emissions reductions while helping the most vulnerable countries adapt to climate change.And it is clearly up to the current generation of leaders from high-emitting nations to decide whether they want to be remembered as climates change tyrants or pioneers.46. The author is critical of the Paris climate agreement because_____.A．it is unfair to those climate-vulnerable nations.B．it aims to keep temperature rise blew 2℃ only.C．it is beneficial to only fewer than 4% of countries.D．it burdens developed countries with sole responsibility.47. Why does the author call some developed countries climate "free-riders"? A．They needn't worry about the food and water they consume.B．They are better able to cope with the global climate change.C．They hardly pay anything for the problems they have caused.D．They are free from the greenhouse effects affecting "forced riders".48. Why does the author compare the " forced riders " to second-hand smokers? A．They have little responsibility for public health problems.B．They are vulnerable to unhealthy environmental conditions.C．They have to bear consequence they are not responsible for.D．They are unaware of the potential risks they are confronting.49. What does the author say about the $100 billion funding?A．It will motivate all nations to reduce carbon emissions.B．There is no final agreement on where it will come from.C．There is no clarification of how the money will be spent.D．It will effectively reduce greenhouse emissions worldwide.50. What urgent action must be taken to realise the Paris climate agreement? A．Encouraging high-emitting nations to take the initiative.B．Calling on all the nations concerned to make joint efforts.C．Pushing the current world leaders to come to a consensus.D．Putting in effect the policies in the agreement at once.（二） 1.Teenagers at risk of depression, anxiety and suicide often wear their troubles like a neon（霓虹灯）sign. Their risky behaviors－drinking too much alcohol, using illegal drugs , smoking cigarettes and skipping school－can alert parents and teachers that serious problems are brewing.2.But a new study finds that there's another group of adolescents who are in nearly as much danger of experience the same psychiatric symptoms: teens who use tons ofmedia, don't get enough sleep and have a sedentary（不爱活动的）lifestyle.3.Of course, that may sound like a description of every teenager on the planet. But the study warns that it is teenagers who engage in all three of these practices in the extreme who are truly in jeopardy. Because their behavior are not usually seen as a red flag. These young people have been dubbed the "invisible risk" group by the study's authors.4."In some ways they're at greater risk of falling through the cracks," says researcher Vladimir Cali. "while most parents, teachers and clinicians would react to an adolescent using drugs or getting drunk, they may easily overlook teenagers who are engaging in inconspicuous behaviors."5.The study's authors surveyed 12,395 students and analyzed nine risk behaviors , including excessive alcohol use,illegal drug use, heavy smoking ,high media use and truancy（逃课）. Their aim was to determine the relationship between these risk behaviors and mental health issues in teenagers.6.About 58% of the students demonstrated none or few of the risk behaviors. Some 13% scored high on all nine of the risk behaviors. And 29%, the "invisible risk" group, scored high on three in particular: They spent five hours a day or more on electronic devices. They slept six hours a night or less. And they neglected "other healthy activities."7.The group that scored high on all nine of the risk behaviors was most likely to show symptoms of depression; in all, nearly 15% of this group reported being depressed, compared with just 4% of the low-risk group. But the invisible group wasn't far behind the high-risk set, with more than 13% of them exhibiting depression.8.The findings caught Cali off guard. "We were very surprised," he says. "The high-risk group and low-risk group are obvious. But this third group was not only unexpected, it was so distinct and so large－nearly one third of our sample－that itbecame a key finding of the study."9.Cali says that one of the most significant things about his study is that it provides new early-warning signs for parents, teachers and mental health-care providers. And early identification, support and treatment for mental health issues, he says, are the best ways to keep them from turning into full-blown disorders.51. What does the author mean by saying "Teenagers at risk of depression, anxiety and suicide often wear their troubles like a neon sign" (Lines 1-2, Para.1)? A．Mental problems can now be found in large numbers of teenagers B．Teenagers' mental problems are getting more and more attention C．Teenagers' mental problems are often too conspicuous not to be observed. D．Depression and anxiety are themost common symptoms of mental problems. 52. What is the finding of the new study?A．Teenagers' lifestyles have changes greatly in recent years.B．Many teenagers resort to drugs or alcohol for mental relief.C．Teenagers experiencing psychological problems tend to use a lot of media. D．Many hitherto unobserved youngsters may have psychological problems.53. Why do the researchers refer to teens who use tons of media, don't get enough sleep and have a sedentary lifestyle as the "invisible risk" group?A．Their behaviors can be an invisible threat to society.B．Their behaviors do not constitute a warning signal.C．Their behaviors do not tend towards mental problems.D．Their behaviors can be found in almost all teenagers on earth.54. What does the new studyfind about the invisible group?A．They are almost as liable to depression as the high-risk group.B．They suffer from depression without showing any symptoms.C．They do not often demonstrate risky behaviors as their peers.D．They do not attract the media attention the high-risk group does.55. What is the significance of Vladimir Carl's study?A．It offers a new treatment for psychological problems among teenagers.B．It provides new early-warning signals for identifying teens in trouble.C．It may have found an ideal way to handle teenagers with behavioral problems. D．It sheds new light on how unhealthy behaviors trigger mental health problems.。
二维目标视觉检测与跟踪系统设计的开题报告一、题目背景近年来，随着机器人技术的不断发展，机器人已经广泛应用于各个领域，如工业、医疗、服务等。
在机器人领域中，视觉检测与跟踪技术是非常重要的一部分，对于机器人的感知、判断和决策都有着至关重要的作用。
在物体检测与跟踪中，二维目标视觉检测与跟踪系统是关键技术之一，可以在机器人场景中高效、准确地实现对目标的检测、追踪和识别。
因此，设计一种高效、准确的二维目标视觉检测与跟踪系统对于推进机器人技术的发展和应用具有重要的意义。
二、研究目的和意义本研究的主要目的是设计一种高效、准确的二维目标视觉检测与跟踪系统。
为了达到这一目的，我们将运用深度学习等相关技术，对图像进行特征提取和分类，采用跟踪算法对目标进行跟踪，以实现对目标的检测、追踪和识别。
该系统可广泛应用于机器人领域中，如自主导航、工业自动化、智能监控等各个方面。
三、研究内容和方法1. 系统需求分析：分析机器人应用场景和用户需求，确定系统设计的目标和功能。
2. 图像采集与预处理：使用相机或其他采集设备获取场景图像，并对图像进行预处理，如噪声过滤、亮度调整等。
3. 物体检测与分类：基于深度学习等相关技术，对图像中的目标进行特征提取和分类，实现目标物体的快速、准确检测。
4. 目标跟踪算法：根据目标特征和运动状态，采用跟踪算法对目标进行跟踪，实时更新目标位置和状态。
5. 系统实现与测试：在实际场景中实现系统功能，并进行测试，评估系统性能和效果。
四、研究进度安排阶段目标时间安排1. 系统需求分析和文献调研第一周 ~ 第二周2. 图像采集与预处理第三周 ~ 第四周3. 物体检测与分类第五周 ~ 第六周4. 目标跟踪算法第七周 ~ 第八周5. 系统实现与测试第九周 ~ 第十周6. 系统优化和性能评估第十一周 ~ 第十二周七、参考文献[1] Gao Y, Bronstein M, Bronstein A, et al. Deep learning for object tracking: A survey. arXiv preprint arXiv:1809.04436, 2018.[2] Ren S, He K, Girshick R, et al. Faster R-CNN: towards real-time object detection with region proposal networks[J]. IEEE transactions on pattern analysis and machine intelligence, 2017, 39(6): 1137-1149.[3] Li W, Li J, Lu H, et al. DeepID-Net: Object detection with deformable part based convolutional neural networks[J]. IEEE transactions on pattern analysis and machine intelligence, 2017, 39(7): 1320-1334.[4] Bagdanov A D, del Bimbo A. Fast and effective object detection under low illumination conditions[C]//4th International Workshop on Visual Surveillance. 2007: 23-30.[5] Chu D, Porikli F. Small target detection using L1-norm maximization[C]//European conference on computer vision. Springer, Cham, 2014: 439-454.。
无线传感器网络定位算法研究的开题报告一、研究背景随着无线传感器网络（Wireless Sensor Network，WSN）的广泛应用，WSN的定位问题也成为一个热门研究领域。
WSN定位技术可以广泛应用于环境监测、资源管理、安全防范等领域。
然而，由于无线信号在传输过程中易受到干扰、衰减和多径效应等因素的影响，导致无法直接使用三角定位法等经典的定位方法。
因此，研究开发适用于WSN的定位方法具有重要的理论价值和实际应用价值。
二、研究内容本研究将重点研究以下内容：1. WSN定位算法的理论分析与设计，包括基于距离测量、角度测量和时间差测量等不同方式的定位算法，并对各种算法的优缺点进行比较和分析。
2. WSN中节点部署方式对定位算法的影响，探究网络结构与节点分布对定位算法性能的影响规律，对不同部署方式的优化策略进行研究和设计。
3. 基于WSN定位算法的应用研究，探究不同应用场景下的WSN定位需求和实现方式，并进行实验验证。
三、研究方法本研究将采用以下方法：1. 文献调研法，对WSN定位算法的研究现状、前沿进展和相关技术进行综述和归纳，为后续实验和数据分析提供理论基础。
2. 理论分析法，根据WSN定位算法的理论模型和特点，进行算法优化、改进和创新设计。
3. 数值模拟法，通过计算机仿真和数据分析，验证WSN定位算法的可行性和性能优化效果，为应用实验提供实验构架和方案。
四、研究意义本研究的主要意义包括：1. 为WSN定位算法的研究提供新思路和新方法，为WSN应用场景的实际需求提供技术支持。
2. 推动WSN的发展和应用，提高WSN的定位精度和稳定性，为环境监测、资源管理、安全防范等领域的实践应用提供技术支持。
3. 为网络通信、计算机科学等领域的理论研究提供实验数据和实际案例，拓宽研究领域和视野。
五、研究进度安排本研究计划总时长为一年，具体进度安排如下：1. 第一阶段（2个月）：收集和调研WSN定位算法的相关文献和实验数据，对各种算法进行比较和分析。
Object Tracking:A SurveyAlper YilmazOhio State UniversityOmar JavedObjectVideo,Inc.andMubarak ShahUniversity of Central FloridaThe goal of this article is to review the state-of-the-art tracking methods,classify them into different cate-gories,and identify new trends.Object tracking,in general,is a challenging problem.Difficulties in tracking objects can arise due to abrupt object motion,changing appearance patterns of both the object and the scene, nonrigid object structures,object-to-object and object-to-scene occlusions,and camera motion.Tracking is usually performed in the context of higher-level applications that require the location and/or shape of the object in every frame.Typically,assumptions are made to constrain the tracking problem in the context of a particular application.In this survey,we categorize the tracking methods on the basis of the object and motion representations used,provide detailed descriptions of representative methods in each category,and examine their pros and cons.Moreover,we discuss the important issues related to tracking including the use of appropriate image features,selection of motion models,and detection of objects.Categories and Subject Descriptors:I.4.8[Image Processing and Computer Vision]:Scene Analysis—TrackingGeneral Terms:AlgorithmsAdditional Key Words and Phrases:Appearance models,contour evolution,feature selection,object detection, object representation,point tracking,shape trackingACM Reference Format:Yilmaz,A.,Javed,O.,and Shah,M.2006.Object tracking:A survey.ACM Comput.Surv.38,4,Article13 (Dec.2006),45pages.DOI=10.1145/1177352.1177355/10.1145/1177352.1177355This material is based on work funded in part by the US Government.Any opinions,findings,and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the US Government.Author’s address:A.Yilmaz,Department of CEEGS,Ohio State University;email:yilmaz.15@; O.Javed,ObjectVideo,Inc.,Reston,VA20191;email:ojaved@;M.Shah,School of EECS, University of Central Florida;email:shah@.Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or direct commercial advantage and that copies show this notice on thefirst page or initial screen of a display along with the full citation.Copyrights for components of this work owned by others than ACM must be honored.Abstracting with credit is per-mitted.To copy otherwise,to republish,to post on servers,to redistribute to lists,or to use any component of this work in other works requires prior specific permission and/or a fee.Permissions may be requested from Publications Dept.,ACM,Inc.,2Penn Plaza,Suite701,New York,NY10121-0701USA,fax+1(212) 869-0481,or permissions@.c 2006ACM0360-0300/2006/12-ART13$5.00DOI:10.1145/1177352.1177355/10.1145/ 1177352.1177355.ACM Computing Surveys,Vol.38,No.4,Article13,Publication date:December2006.2 A.Yilmaz et al.1.INTRODUCTIONObject tracking is an important task within thefield of computer vision.The prolif-eration of high-powered computers,the availability of high quality and inexpensive video cameras,and the increasing need for automated video analysis has generated a great deal of interest in object tracking algorithms.There are three key steps in video analysis:detection of interesting moving objects,tracking of such objects from frame to frame,and analysis of object tracks to recognize their behavior.Therefore,the use of object tracking is pertinent in the tasks of:—motion-based recognition,that is,human identification based on gait,automatic ob-ject detection,etc;—automated surveillance,that is,monitoring a scene to detect suspicious activities or unlikely events;—video indexing,that is,automatic annotation and retrieval of the videos in multimedia databases;—human-computer interaction,that is,gesture recognition,eye gaze tracking for data input to computers,etc.;—traffic monitoring,that is,real-time gathering of traffic statistics to direct trafficflow.—vehicle navigation,that is,video-based path planning and obstacle avoidance capabilities.In its simplest form,tracking can be defined as the problem of estimating the tra-jectory of an object in the image plane as it moves around a scene.In other words,a tracker assigns consistent labels to the tracked objects in different frames of a video.Ad-ditionally,depending on the tracking domain,a tracker can also provide object-centric information,such as orientation,area,or shape of an object.Tracking objects can be complex due to:—loss of information caused by projection of the3D world on a2D image,—noise in images,—complex object motion,—nonrigid or articulated nature of objects,—partial and full object occlusions,—complex object shapes,—scene illumination changes,and—real-time processing requirements.One can simplify tracking by imposing constraints on the motion and/or appearance of objects.For example,almost all tracking algorithms assume that the object motion is smooth with no abrupt changes.One can further constrain the object motion to be of constant velocity or constant acceleration based on a priori information.Prior knowledge about the number and the size of objects,or the object appearance and shape,can also be used to simplify the problem.Numerous approaches for object tracking have been proposed.These primarily differ from each other based on the way they approach the following questions:Which object representation is suitable for tracking?Which image features should be used?How should the motion,appearance,and shape of the object be modeled?The answers to these questions depend on the context/environment in which the tracking is performed and the end use for which the tracking information is being sought.A large number of tracking methods have been proposed which attempt to answer these questions for a variety of scenarios.The goal of this survey is to group tracking methods into broadACM Computing Surveys,Vol.38,No.4,Article13,Publication date:December2006.Object Tracking:A Survey3 categories and provide comprehensive descriptions of representative methods in each category.We aspire to give readers,who require a tracker for a certain application, the ability to select the most suitable tracking algorithm for their particular needs. Moreover,we aim to identify new trends and ideas in the tracking community and hope to provide insight for the development of new tracking methods.Our survey is focused on methodologies for tracking objects in general and not on trackers tailored for specific objects,for example,person trackers that use human kine-matics as the basis of their implementation.There has been substantial work on track-ing humans using articulated object models that has been discussed and categorized in the surveys by Aggarwal and Cai[1999],Gavrilla[1999],and Moeslund and Granum [2001].We will,however,include some works on the articulated object trackers that are also applicable to domains other than articulated objects.We follow a bottom-up approach in describing the issues that need to be addressed when one sets out to build an object tracker.Thefirst issue is defining a suitable rep-resentation of the object.In Section2,we will describe some common object shape representations,for example,points,primitive geometric shapes and object contours, and appearance representations.The next issue is the selection of image features used as an input for the tracker.In Section3,we discuss various image features,such as color, motion,edges,etc.,which are commonly used in object tracking.Almost all tracking algorithms require detection of the objects either in thefirst frame or in every frame. Section4summarizes the general strategies for detecting the objects in a scene.The suitability of a particular tracking algorithm depends on object appearances,object shapes,number of objects,object and camera motions,and illumination conditions.In Section5,we categorize and describe the existing tracking methods and explain their strengths and weaknesses in a summary section at the end of each category.In Section 6,important issues relevant to object tracking are discussed.Section7presents future directions in tracking research.Finally,concluding remarks are sketched in Section8.2.OBJECT REPRESENTATIONIn a tracking scenario,an object can be defined as anything that is of interest for further analysis.For instance,boats on the sea,fish inside an aquarium,vehicles on a road, planes in the air,people walking on a road,or bubbles in the water are a set of objects that may be important to track in a specific domain.Objects can be represented by their shapes and appearances.In this section,we willfirst describe the object shape representations commonly employed for tracking and then address the joint shape and appearance representations.—Points.The object is represented by a point,that is,the centroid(Figure1(a)) [Veenman et al.2001]or by a set of points(Figure1(b))[Serby et al.2004].In general, the point representation is suitable for tracking objects that occupy small regions in an image.(see Section5.1).—Primitive geometric shapes.Object shape is represented by a rectangle,ellipse (Figure1(c),(d)[Comaniciu et al.2003],etc.Object motion for such representations is usually modeled by translation,affine,or projective(homography)transformation (see Section5.2for details).Though primitive geometric shapes are more suitable for representing simple rigid objects,they are also used for tracking nonrigid objects.—Object silhouette and contour.Contour representation defines the boundary of an object(Figure1(g),(h).The region inside the contour is called the silhouette of the object(see Figure1(i)).Silhouette and contour representations are suitable for track-ing complex nonrigid shapes[Yilmaz et al.2004].ACM Computing Surveys,Vol.38,No.4,Article13,Publication date:December2006.4 A.Yilmaz et al.Fig.1.Object representations.(a)Centroid,(b)multiple points,(c)rectangularpatch,(d)elliptical patch,(e)part-based multiple patches,(f)object skeleton,(g)complete object contour,(h)control points on object contour,(i)object silhouette.—Articulated shape models.Articulated objects are composed of body parts that are held together with joints.For example,the human body is an articulated object with torso,legs,hands,head,and feet connected by joints.The relationship between the parts are governed by kinematic motion models,for example,joint angle,etc.In order to represent an articulated object,one can model the constituent parts using cylinders or ellipses as shown in Figure1(e).—Skeletal models.Object skeleton can be extracted by applying medial axis trans-form to the object silhouette[Ballard and Brown1982,Chap.8].This model is com-monly used as a shape representation for recognizing objects[Ali and Aggarwal2001]. Skeleton representation can be used to model both articulated and rigid objects(see Figure1(f).There are a number of ways to represent the appearance features of objects.Note that shape representations can also be combined with the appearance representations [Cootes et al.2001]for tracking.Some common appearance representations in the context of object tracking are:—Probability densities of object appearance.The probability density estimates of the object appearance can either be parametric,such as Gaussian[Zhu and Yuille1996] and a mixture of Gaussians[Paragios and Deriche2002],or nonparametric,such as Parzen windows[Elgammal et al.2002]and histograms[Comaniciu et al.2003].The probability densities of object appearance features(color,texture)can be computed from the image regions specified by the shape models(interior region of an ellipse or a contour).—Templates.Templates are formed using simple geometric shapes or silhouettes [Fieguth and Terzopoulos1997].An advantage of a template is that it carries both spatial and appearance information.Templates,however,only encode the object ap-pearance generated from a single view.Thus,they are only suitable for tracking objects whose poses do not vary considerably during the course of tracking.ACM Computing Surveys,Vol.38,No.4,Article13,Publication date:December2006.Object Tracking:A Survey5—Active appearance models.Active appearance models are generated by simultane-ously modeling the object shape and appearance[Edwards et al.1998].In general, the object shape is defined by a set of landmarks.Similar to the contour-based repre-sentation,the landmarks can reside on the object boundary or,alternatively,they can reside inside the object region.For each landmark,an appearance vector is stored which is in the form of color,texture,or gradient magnitude.Active appearance models require a training phase where both the shape and its associated appear-ance is learned from a set of samples using,for instance,the principal component analysis.—Multiview appearance models.These models encode different views of an object.One approach to represent the different object views is to generate a subspace from the given views.Subspace approaches,for example,Principal Component Analysis(PCA) and Independent Component Analysis(ICA),have been used for both shape and appearance representation[Mughadam and Pentland1997;Black and Jepson1998]. Another approach to learn the different views of an object is by training a set of clas-sifiers,for example,the support vector machines[Avidan2001]or Bayesian networks [Park and Aggarwal2004].One limitation of multiview appearance models is that the appearances in all views are required ahead of time.In general,there is a strong relationship between the object representations and the tracking algorithms.Object representations are usually chosen according to the ap-plication domain.For tracking objects,which appear very small in an image,point representation is usually appropriate.For instance,Veenman et al.[2001]use the point representation to track the seeds in a moving dish sequence.Similarly,Shafique and Shah[2003]use the point representation to track distant birds.For the objects whose shapes can be approximated by rectangles or ellipses,primitive geometric shape representations are more aniciu et al.[2003]use an elliptical shape representation and employ a color histogram computed from the elliptical region for modeling the appearance.In1998,Black and Jepson used eigenvectors to represent the appearance.The eigenvectors were generated from rectangular object templates.For tracking objects with complex shapes,for example,humans,a contour or a silhouette-based representation is appropriate.Haritaoglu et al.[2000]use silhouettes for object tracking in a surveillance application.3.FEATURE SELECTION FOR TRACKINGSelecting the right features plays a critical role in tracking.In general,the most de-sirable property of a visual feature is its uniqueness so that the objects can be easily distinguished in the feature space.Feature selection is closely related to the object rep-resentation.For example,color is used as a feature for histogram-based appearance representations,while for contour-based representation,object edges are usually used as features.In general,many tracking algorithms use a combination of these features. The details of common visual features are as follows.—Color.The apparent color of an object is influenced primarily by two physical factors, 1)the spectral power distribution of the illuminant and2)the surface reflectance properties of the object.In image processing,the RGB(red,green,blue)color space is usually used to represent color.However,the RGB space is not a perceptually uni-form color space,that is,the differences between the colors in the RGB space do not correspond to the color differences perceived by humans[Paschos2001].Addition-ally,the RGB dimensions are highly correlated.In contrast,L∗u∗v∗and L∗a∗b∗are perceptually uniform color spaces,while HSV(Hue,Saturation,Value)is an approx-imately uniform color space.However,these color spaces are sensitive to noise[Song ACM Computing Surveys,Vol.38,No.4,Article13,Publication date:December2006.6 A.Yilmaz et al. et al.1996].In summary,there is no last word on which color space is more efficient, therefore a variety of color spaces have been used in tracking.—Edges.Object boundaries usually generate strong changes in image intensities.Edge detection is used to identify these changes.An important property of edges is that they are less sensitive to illumination changes compared to color features.Algorithms that track the boundary of the objects usually use edges as the representative feature. Because of its simplicity and accuracy,the most popular edge detection approach is the Canny Edge detector[Canny1986].An evaluation of the edge detection algorithms is provided by Bowyer et al.[2001].—Optical Flow.Opticalflow is a densefield of displacement vectors which defines the translation of each pixel in a region.It is computed using the brightness constraint, which assumes brightness constancy of corresponding pixels in consecutive frames [Horn and Schunk1981].Opticalflow is commonly used as a feature in motion-based segmentation and tracking applications.Popular techniques for computing dense opticalflow include methods by Horn and Schunck[1981],Lucas and Kanade[1981], Black and Anandan[1996],and Szeliski and Couglan[1997].For the performance evaluation of the opticalflow methods,we refer the interested reader to the survey by Barron et al.[1994].—Texture.Texture is a measure of the intensity variation of a surface which quantifies properties such as smoothness and pared to color,texture requires a processing step to generate the descriptors.There are various texture descriptors: Gray-Level Cooccurrence Matrices(GLCM’s)[Haralick et al.1973](a2D histogram which shows the cooccurrences of intensities in a specified direction and distance), Law’s texture measures[Laws1980](twenty-five2Dfilters generated fromfive1D filters corresponding to level,edge,spot,wave,and ripple),wavelets[Mallat1989] (orthogonal bank offilters),and steerable pyramids[Greenspan et al.1994].Simi-lar to edge features,the texture features are less sensitive to illumination changes compared to color.Mostly features are chosen manually by the user depending on the application domain.However,the problem of automatic feature selection has received signif-icant attention in the pattern recognition community.Automatic feature selection methods can be divided intofilter methods and wrapper methods[Blum and Langley 1997].Thefilter methods try to select the features based on a general criteria,for ex-ample,the features should be uncorrelated.The wrapper methods select the features based on the usefulness of the features in a specific problem domain,for example, the classification performance using a subset of features.Principal Component Analy-sis(PCA)is an example of thefilter methods for the feature reduction.PCA involves transformation of a number of(possibly)correlated variables into a(smaller)number of uncorrelated variables called the principal components.Thefirst principal component accounts for as much of the variability in the data as possible,and each succeeding component accounts for as much of the remaining variability as possible.A wrapper method of selecting the discriminatory features for tracking a particular class of ob-jects is the Adaboost[Tieu and Viola2004]algorithm.Adaboost is a method forfinding a strong classifier based on a combination of moderately inaccurate weak classifiers. Given a large set of features,one classifier can be trained for each feature.Adaboost, as discussed in Sections4.4,will discover a weighted combination of classifiers(repre-senting features)that maximize the classification performance of the algorithm.The higher the weight of the feature,the more discriminatory it is.One can use thefirst n highest-weighted features for tracking.ACM Computing Surveys,Vol.38,No.4,Article13,Publication date:December2006.Object Tracking:A Survey7Table I.Object Detection CategoriesCategories Representative WorkPoint detectors Moravec’s detector[Moravec1979],Harris detector[Harris and Stephens1988],Scale Invariant Feature Transform[Lowe2004].Affine Invariant Point Detector[Mikolajczyk and Schmid2002].Segmentation Mean-shift[Comaniciu and Meer1999],Graph-cut[Shi and Malik2000],Active contours[Caselles et al.1995].Background Modeling Mixture of Gaussians[Stauffer and Grimson2000],Eigenbackground[Oliver et al.2000],Wallflower[Toyama et al.1999],Dynamic texture background[Monnet et al.2003].Supervised Classifiers Support Vector Machines[Papageorgiou et al.1998],Neural Networks[Rowley et al.1998],Adaptive Boosting[Viola et al.2003].Among all features,color is one of the most widely used feature for tracking. Comaniciu et al.[2003]use a color histogram to represent the object appearance.De-spite its popularity,most color bands are sensitive to illumination variation.Hence in scenarios where this effect is inevitable,other features are incorporated to model object appearance.Cremers et al.[2003]use opticalflow as a feature for contour track-ing.Jepson et al.[2003]use steerablefilter responses for tracking.Alternatively,a combination of these features is also utilized to improve the tracking performance. 4.OBJECT DETECTIONEvery tracking method requires an object detection mechanism either in every frame or when the objectfirst appears in the video.A common approach for object detection is to use information in a single frame.However,some object detection methods make use of the temporal information computed from a sequence of frames to reduce the number of false detections.This temporal information is usually in the form of frame differencing, which highlights changing regions in consecutive frames.Given the object regions in the image,it is then the tracker’s task to perform object correspondence from one frame to the next to generate the tracks.We tabulate several common object detection methods in Table I.Although the object detection itself requires a survey of its own,here we outline the popular methods in the context of object tracking for the sake of completeness.4.1.Point DetectorsPoint detectors are used tofind interest points in images which have an expressive texture in their respective localities.Interest points have been long used in the con-text of motion,stereo,and tracking problems.A desirable quality of an interest point is its invariance to changes in illumination and camera viewpoint.In the literature, commonly used interest point detectors include Moravec’s interest operator[Moravec 1979],Harris interest point detector[Harris and Stephens1988],KLT detector[Shi and Tomasi1994],and SIFT detector[Lowe2004].For a comparative evaluation of interest point detectors,we refer the reader to the survey by Mikolajczyk and Schmid [2003].Tofind interest points,Moravec’s operator computes the variation of the image inten-sities in a4×4patch in the horizontal,vertical,diagonal,and antidiagonal directions and selects the minimum of the four variations as representative values for the window.A point is declared interesting if the intensity variation is a local maximum in a12×12patch.ACM Computing Surveys,Vol.38,No.4,Article13,Publication date:December2006.8 A.Yilmaz etal.Fig.2.Interest points detected by applying(a)the Harris,(b)the KLT,and(c)SIFT operators.The Harris detector computes thefirst order image derivatives,(I x,I y),in x and y di-rections to highlight the directional intensity variations,then a second moment matrix, which encodes this variation,is evaluated for each pixel in a small neighborhood:M=I2xI x I yI x I yI2y.(1)An interest point is identified using the determinant and the trace of M which mea-sures the variation in a local neighborhood R=det(M)−k·tr(M)2,where k is constant.The interest points are marked by thresholding R after applying nonmaxima suppres-sion(see Figure2(a)for results).The source code for Harris detector is available atHarrisSrc.The same moment matrix M given in Equation(1)is used in the interestpoint detection step of the KLT tracking method.Interest point confidence,R,is com-puted using the minimum eigenvalue of M,λmin.Interest point candidates are selectedby thresholding R.Among the candidate points,KLT eliminates the candidates thatare spatially close to each other(Figure2(b)).Implementation of the KLT detector isavailable at KLTSrc.Quantitatively both Harris and KLT emphasize the intensity variations using verysimilar measures.For instance,R in Harris is related to the characteristic polynomialused forfinding the eigenvalues of M:λ2+det(M)−λ·tr(M)=0,while KLT computes the eigenvalues directly.In practice,both of these methodsfind almost the same interestpoints.The only difference is the additional KLT criterion that enforces a predefinedspatial distance between detected interest points.In theory,the M matrix is invariant to both rotation and translation.However,itis not invariant to affine or projective transformations.In order to introduce robustdetection of interest points under different transformations,Lowe[2004]introducedthe SIFT(Scale Invariant Feature Transform)method which is composed of four steps.First,a scale space is constructed by convolving the image with Gaussianfilters atdifferent scales.Convolved images are used to generate difference-of-Gaussians(DoG)images.Candidate interest points are then selected from the minima and maxima ofthe DoG images across scales.The next step updates the location of each candidate byinterpolating the color values using neighboring pixels.In the third step,low contrastcandidates as well as the candidates along the edges are eliminated.Finally,remaininginterest points are assigned orientations based on the peaks in the histograms ofgradient directions in a small neighborhood around a candidate point.SIFT detectorgenerates a greater number of interest points compared to other interest point detec-tors.This is due to the fact that the interest points at different scales and differentresolutions(pyramid)are accumulated.Empirically,it has been shown in Mikolajczykand Schmid[2003]that SIFT outperforms most point detectors and is more resilientto image deformations.Implementation of the SIFT detector is available at SIFTSrc.ACM Computing Surveys,Vol.38,No.4,Article13,Publication date:December2006.Object Tracking:A Survey9Fig.3.Mixture of Gaussian modeling for background subtraction.(a)Image from a sequencein which a person is walking across the scene.(b)The mean of the highest-weighted Gaussiansat each pixels position.These means represent the most temporally persistent per-pixel colorand hence should represent the stationary background.(c)The means of the Gaussian withthe second-highest weight;these means represent colors that are observed less frequently.(d)Background subtraction result.The foreground consists of the pixels in the current frame thatmatched a low-weighted Gaussian.4.2.Background SubtractionObject detection can be achieved by building a representation of the scene called the background model and thenfinding deviations from the model for each incoming frame. Any significant change in an image region from the background model signifies a moving object.The pixels constituting the regions undergoing change are marked for further ually,a connected component algorithm is applied to obtain connected regions corresponding to the objects.This process is referred to as the background subtraction.Frame differencing of temporally adjacent frames has been well studied since the late70s[Jain and Nagel1979].However,background subtraction became popular fol-lowing the work of Wren et al.[1997].In order to learn gradual changes in time,Wren et al.propose modeling the color of each pixel,I(x,y),of a stationary background with a single3D(Y,U,and V color space)Gaussian,I(x,y)∼N(μ(x,y), (x,y)).The model parameters,the meanμ(x,y)and the covariance (x,y),are learned from the color observations in several consecutive frames.Once the background model is de-rived,for every pixel(x,y)in the input frame,the likelihood of its color coming from N(μ(x,y), (x,y))is computed,and the pixels that deviate from the background model are labeled as the foreground pixels.However,a single Gaussian is not a good model for outdoor scenes[Gao et al.2000]since multiple colors can be observed at a certain loca-tion due to repetitive object motion,shadows,or reflectance.A substantial improvement in background modeling is achieved by using multimodal statistical models to describe per-pixel background color.For instance,Stauffer and Grimson[2000]use a mixture of Gaussians to model the pixel color.In this method,a pixel in the current frame is checked against the background model by comparing it with every Gaussian in the model until a matching Gaussian is found.If a match is found,the mean and vari-ance of the matched Gaussian is updated,otherwise a new Gaussian with the mean equal to the current pixel color and some initial variance is introduced into the mix-ture.Each pixel is classified based on whether the matched distribution represents the background process.Moving regions,which are detected using this approach,along with the background models are shown in Figure3.Another approach is to incorporate region-based(spatial)scene information instead of only using color-based information.Elgammal and Davis[2000]use nonparamet-ric kernel density estimation to model the per-pixel background.During the sub-traction process,the current pixel is matched not only to the corresponding pixel in the background model,but also to the nearby pixel locations.Thus,this method can handle camera jitter or small movements in the background.Li and Leung[2002] fuse the texture and color features to perform background subtraction over blocks of ACM Computing Surveys,Vol.38,No.4,Article13,Publication date:December2006.。
