Real-time Multiple Objects Tracking with Occlusion Handling in Dynamic Scenes

物体位姿估计综述英文Overview of Object Pose Estimation.Object pose estimation is a crucial task in computer vision, aiming to determine the position and orientation of objects in a given scene. It plays a pivotal role in various applications, ranging from augmented reality to robotics and autonomous driving. This article presents a comprehensive overview of object pose estimation, discussing its importance, methods, challenges, and future trends.Importance of Object Pose Estimation.Object pose estimation is essential for understanding and interacting with the physical world. It enables systems to perceive the three-dimensional position and orientation of objects accurately, enabling precise manipulation, localization, and tracking. In augmented reality, pose estimation is crucial for overlaying virtual objects ontothe real world. In robotics, it enables robots to grasp, manipulate, and interact with objects effectively. In autonomous driving, pose estimation is vital for perceiving the position and orientation of vehicles and pedestrians to ensure safe navigation.Methods of Object Pose Estimation.Object pose estimation can be categorized into two broad approaches: template-based methods and learning-based methods.Template-based methods involve the creation of a 3D model or template of the object and matching it with the observed 2D image to estimate the pose. One popular algorithm is the Iterative Closest Point (ICP), whichaligns the 3D model with the 2D image by minimizing the distances between corresponding points. Template-based methods are accurate but computationally expensive and limited to known object categories.Learning-based methods, on the other hand, utilize deeplearning techniques to learn pose estimation directly from data. Convolutional Neural Networks (CNNs) are commonly used to extract features from images, and pose estimationis performed using regression or classification tasks. Methods like PoseNet and PVNet have achieved remarkable results in recent years. Learning-based methods are more flexible and can handle unknown object categories but require large amounts of labeled data for training.Challenges in Object Pose Estimation.Object pose estimation faces several challenges, including occlusion, cluttered scenes, and varying lighting conditions. Occlusion occurs when objects overlap or are partially hidden, making it difficult to extract sufficient information for pose estimation. Cluttered scenes present a challenge due to the presence of multiple objects, makingit difficult to separate and identify individual objects. Varying lighting conditions can affect the appearance of objects, leading to inaccuracies in pose estimation.Another challenge is the diversity of object shapes andsizes. Different objects have unique geometric properties that require specific approaches for accurate pose estimation. Additionally, pose estimation is oftensensitive to noise and outliers in the input data, which can affect the accuracy of the estimated pose.Future Trends in Object Pose Estimation.With the advancements in deep learning and computer vision, object pose estimation is expected to evolvefurther in the coming years. One promising direction is the integration of sensor data, such as depth sensors or RGB-D cameras, to enhance pose estimation accuracy in complex environments. Multi-modal data fusion can provideadditional information about object geometry and depth, leading to more robust pose estimation.Another trend is the utilization of larger and more diverse datasets for training deep learning models. This will enable the development of more generalizable and robust pose estimation algorithms that can handle a wide range of object categories and environments.Finally, real-time pose estimation is an important direction for future research. Many applications, such as augmented reality and robotics, require pose estimation to be performed in real-time, enabling fast and responsive interactions. The development of efficient algorithms and hardware optimizations can lead to significant improvements in real-time pose estimation capabilities.In conclusion, object pose estimation is a crucial task in computer vision with widespread applications. It involves the estimation of the position and orientation of objects in a given scene, enabling precise manipulation, localization, and tracking. Template-based and learning-based methods are commonly used for pose estimation, each with its own advantages and limitations. Challenges such as occlusion, cluttered scenes, and varying lighting conditions need to be addressed to improve pose estimation accuracy. Future trends include the integration of sensor data, utilization of larger and more diverse datasets, and the development of real-time pose estimation algorithms. With continued research and advancements in this field,object pose estimation is expected to play a pivotal role in enabling more intelligent and responsive interactions with the physical world.。

1 Real-Time Tracking via On-line Boosting Helmut Grabner,Michael Grabner,Horst BischofInstitute for Computer Graphics and VisionGraz University of Technology{hgrabner,mgrabner,bischof}@icg.tu-graz.ac.atAbstractVery recently tracking was approached using classification techniques suchas support vector machines.The object to be tracked is discriminated by aclassifier from the background.In a similar spirit we propose a novel on-lineAdaBoost feature selection algorithm for tracking.The distinct advantage ofour method is its capability of on-line training.This allows to adapt the clas-sifier while tracking the object.Therefore appearance changes of the object(e.g.out of plane rotations,illumination changes)are handled quite naturally.Moreover,depending on the background the algorithm selects the most dis-criminating features for tracking resulting in stable tracking results.By usingfast computable features(e.g.Haar-like wavelets,orientation histograms,lo-cal binary patterns)the algorithm runs in real-time.We demonstrate the per-formance of the algorithm on several(publically available)video sequences.1IntroductionThe efficient and robust tracking of objects in complex environments is important for a variety of applications including video surveillance[25],autonomous driving[1]or human-computer interaction[5,4].Thus it is a great challenge to design robust visual tracking methods which can cope with the inevitable variations that can occur in natural scenes such as changes in the illumination,changes of the shape,changes in the view-point,reflectance of the object or partial occlusion of the target.Moreover tracking suc-cess or failure may also depend on how distinguishable an object is from its background. Stated differently,if the object is very distinctive,a simple tracker may already fulfill the requirements.However,having objects similar to the background requires more sophisti-cated features.As a result there is the need for trackers which can handle on the one hand all possible variations of appearance changes of the target object and on the other hand are able to reliably cope with background clutter.Several approaches have been proposed to fulfill these two main requirements for tracking.To cope with appearance variations of the target object during tracking,existing tracking approaches(e.g.[3,8,10])are enhanced by adaptivness to be able to incre-mentally adjust to the changes in the specific tracking environment(e.g.[14,23,17,22, 13,2,15,26]).In other words,invariance against the different variations is obtained by adaptive methods or representations.Many classical algorithms have been modified in order to be able to adjust the tracking algorithm to the tracking environment.The classi-cal subspace tracking approach of Black et al.[3]was enhanced by incremental subspace updating in[14,22].In[14]it is proposed to express the general adaption problem as a2subspace adaption problem,where the visual appearance variations at a short time scale are represented as a linear subspace.In contrast,[26]suggests an on-line selection of local Haar-like features in order to handle possible variations in appearance.In addition,to the on-line adaption problem,recently many techniques have addressed the idea of using information about the background in order to increase the robustness of tracking[1,19,2,26,6].Especially the work of Collins and Liu[6]emphasizes the im-portance of the background appearance.They postulate that the feature space that best distinguishes between object and background is the best feature space to use for track-ing.The idea of considering the tracking problem as a classification problem between object and background has lead to further works[1,2,27]applying well known clas-sifiers to the tracking problem.In[1]a tracker is realized by using a Support Vector Machine which learns off-line to distinguish between the object and the background.The work most closely related to ours is that of[2].Again tracking is considered as a binary classification problem,where an ensemble of weak classifiers is combined into a strong classifier capturing the idea of AdaBoost for selecting discriminative features for tracking. To achieve robustness against appearance variations novel weak classifiers can be added to the ensemble.However,this is done in a batch processing mode using off-line boosting in a batch manner after new training examples have been collected.Moreover the features are quite restricted.The novelty of this paper is to present a real-time object tracking method which is based on a novel on-line version of the AdaBoost algorithm.Our algorithm performs on-line updating of the ensemble of features for the target object during tracking and thus is able to cope with appearance changes of the object.Furthermore,the on-line trained classifier uses the surrounding background as negative examples in the update and becomes therefore very robust against the drifting problem[18].In addition this negative update allows the algorithm to choose the most discriminative features between the object and the background.Therefore the method can deal with both appearance variations of the object and different backgrounds.The algorithm,which uses only grayvalue information (but can also be extended to color),is able to run in real-time since training is simply done by updating the model with the positive and negative examples of the current frame.The reminder of the paper is organized as follows.In Section2we introduce the tracking algorithm and the novel on-line version of AdaBoost for feature selection which forms the bases of the approach.In Section3we show experimental results illustrating the adaptivity,the robustness and the generality of the proposed tracker.2TrackingThe main idea is to formulate the tracking problem as a binary classification task and to achieve robustness by continuously updating the current classifier of the target object. The principle of the tracking approach is depicted in Figure1.Since we are interested in tracking,we assume that the target object has already been detected.This image region is assumed to be a positive image sample for the tracker.At the same time negative examples are extracted by taking regions of the same size as the target window from the surrounding background.These samples are used to make several iterations of the on-line boosting algorithm in order to obtain afirst model which is already stable.Note that these iterations are only necessary for initialization of the tracker.The(a)(b)(c)(d)Figure 1:The four main steps of tracking by a classisifer.Given an initial position of the object (a)in time t ,the classifier is evaluated at many possible positions in a surrounding search region in frame t +1.The achieved confidence map (c)is analyzed in order to estimate the most probable position and finally the tracker (classifier)is updated (d).tracking step is based on the classical approach of template tracking [12].We evaluate the current classifier at a region of interest and obtain for each position a confidence value.We analyze the confidence map and shift the target window to the new location of the maxima.For maximum detection also a mean shift procedure [7]can be ing a motion model for the target object would allow a reduction of the search window.Once the objects has been detected the classifier has to be updated in order to adjust to possible changes in appearance of the target object and to become discriminative to a different background.The current target region is used as a positive update of the classifier while again the surrounding regions represent the negative samples.As new frames arrive,the whole procedure is repeated and the classifier is therefore able to adapt to possible appearance changes and in addition becomes robust against background clutter.Note that the classifier focuses on the current target object while at the same time tries to distinguish the target from its surrounding.Apart from this,tracking of multiple objects is feasible by initializing a separate classifier for each target object.2.1On-line AdaBoostIn this section we briefly review the on-line boosting algorithm (for more details see[11])which allows to generate classifiers that can be efficiently updated by incrementally applying samples.For better understanding of this approach we define the following terms:Weak classifier:A weak classifier has only to perform slightly better than random guess-ing (i.e.,for a binary decision problem,the error rate must be less than 50%).The hypothesis h weak generated by a weak classifier corresponds to a feature and is ob-tained by applying a defined learning algorithm.Selector:Given a set of M weak classifiers with hypothesis H weak ={h weak 1,...,h weak M },a selector selects exactly one of those.h sel (x )=h weak m (x )(1)where m is chosen according to a optimization criterion.In fact we use the esti-mated error e i of each weak classifier h weak i∈H weak such that m =argmin i e i .Strong classifier:Given a set of N weak classifiers,a strong classifier is computed by alinear combination of selectors.Moreover,the value con f (·)(which is related to3Figure2:Principle of on-line boosting for feature selection.the margin)can be interpreted as a confidence measure of the strong classifier.hStrong(x)=sign(con f(x))(2)con f(x)=N∑n=1αn·h sel n(x)(3)The main idea of on-line boosting is the introduction of the so called selectors.They are randomly initialized and each of them holds a seperate feature pool of weak classifiers. When a new training sample arrives the weak classifiers of each selector are updated.The best weak classifier(having the lowest error)is selected by the selector where the error of the weak classifier is estimated from samples seen so far.The complexity is determined by the number of selectors.The part which requires most of the processing time is the updating of weak classifiers. In order to speed up this process,we propose as a modification(similar to[28])to use a single“global weak classifier”pool(see Figure2)which is shared by all selectors instead of single pools for each of them.The advantage of this modification is that now for each sample that arrives,all weak classifiers need to be updated only once.Then the selectors sequentially switch to the best weak classifier with respect to the current estimatedλand the importance weight is passed on to the next selector.This procedure is repeated until all selectors are updated.Finally,at each time step an updated strong classifier is available.In order to increase the diversity of the weak classifiers and to allow changes in the environment,the worst weak classifier of the shared feature pool is replaced with a new randomly chosen one.2.2FeaturesWe use three different types of features for generating weak hypotheses.Haar-like fea-tures like Viola and Jones[24],orientation histograms[16,21,9]and a simple1version of local binary patterns(LBPs)[20].Note,that the computation of all feature types can be 1Binary patterns using a four-neighborhood(i.e.24=16patterns)as a16bin histogram feature.45 done very efficiently using integral images and integral histograms as data structures[21]. This allows to do exhaustive template matching while tracking is still real-time.To obtain a hypothesis from these features we model the probability distribution for positive samples and negative samples.Probability density estimation is done by a Kalmanfiltering technique.For the classic Haar-like wavelets we use a simple threshold and a Bayesian decision criterion as learning algorithm.For the histogram based feature types(orientation histograms and LBPs),we use nearest neighbor learning.Of course, other types and other learning algorithms can be used to obtain a weak hypotheses.(For more details see[11])3Experiments and DiscussionThe experimental section is divided into two parts.First,we perform experiments demon-strating three specific properties of our tracking approach and second we present results on public available sequences for comparison to other tracking approaches.Each tracking task has been initialized by manually marking the target object in thefirst frame.Track-ing has been applied to sequences consisting of hundreds of frames.The performance (speed)depends on the size of the search region which we have defined by enlarging the target region by one third in each direction(for this region the integral representations are computed).In our experiments no motion model has been used.We achieve a frame rate of about20fps.The strong classifier consists of50selectors and the shared feature pool provides250weak classifiers.All experiments have been done on a standard1.6GHz PC with256MB RAM.3.1IllustrationsThe goal of this section is to illustrate three properties of the proposed tracker-adaptivity, robustness and generality.Therefore multiple challenging sequences have been captured with a static camera having a resolution of640×480.AdaptivityTo illustrate the adaptivity of the tracker and its capability to select the best features de-pending on the background,we process a scene where the target object is a small textured patch,see Figure3.The goal of the scenario is to show how the proposed on-line feature selection method can adapt its model to the current tracking problem.Since our approach looks for suitable features which can best discriminate the object from the background, we change the background from a homogeneous to a textured one(same texture as the patch).For evaluation we consider the distribution of the selected feature types(for this experiment we simply used Haar-like features and LBPs).As we can see in the second row of Figure3,the initial choice mainly uses Haar-like features(green)while LBPs(red) are rather rarely used for handling this tracking task.However,after putting texture to the background we can see from the plot in the second row,that the features for tracking the target immediately change.Note that the exchange of features occurs within a quite short period.As a result LBP features have become much more important for the tracking task because the Haar-like features are no longer distriminative.Furthermore the targetobject is still successful tracked even though the texture of the target is the same as in thebackground.(a)Frame7(b)Frame55(c)Frame121(d)Frame152(e)Frame245Figure 3:First row shows the tracked object which is marked with a yellow rectangle.On-line feature selection for tracking is analyzed by considering the ratio of two selected feature types -Haar-like features (dashed green)and local binary patterns (solid red).This experiment shows the importance of on-line learning because we cannot train for all different backgrounds prior tracking starts.In addition,it shows that there is the need of different types of features.RobustnessFor successful real-world tracking a tracker must be able to handle various appearance changes (i.e.:illumination changes,occlusions,out-of-plane rotations,movement)which can occur in natural scenes.Figure 4illustrates the behavior of our proposed method in case of such interferences of the target object.The sequence shows a glass which ini-tially gets occluded (more than 50%)by a paper,afterwards it is moved behind it with additional illumination changes which are caused by the occlusion and finally view-point changes of the target object.The continuous increase of the confidence maximum value in the initial phase (see row 3,image 1)implies the adapting of the tracker to the target object with respect to its background.However if the target object changes its appear-ance or the surrounding background of the target becomes different,the tracker needs to update his features which is reflected in oscillations of the confidence maximum (see row 3)and a flattened confidence map (see row 2).Movement of the tracking target is represented by a shifted peak in the confidence map.To summarize,the tracker is able to handle all kinds of appearance variations of the target object and always aims at finding the best discriminative features for discriminating the target object from the surrounding background.Therefore,the adaptivity is strongly related to the robustness of a tracker.GeneralityIn order to demonstrate the generality we use four different sequences with diverse tar-get objects,see Figure 5.The first sequence,which is depicted in row 1,illustrates the tracking of a tiny Webcam in a cluttered scene.Even though the target object contains few texture and changes in pose occur it is robustly tracked.The second sequence (row 2)67Figure4:Tracking results on a sequence(row1)containing a combination of appearance changes(i.e.illumination,occlusion,movement,out of plane rotation).The behaviour of the proposed tracker is analyzed considering the confidence map(row2)and the maxi-mum confidence value over time(row3).shows the results of tracking a face.As we can see from the depicted frames,the pro-posed approach can cope very well with occlusions which again is the effect of the large number of local features for tracking.Moreover even large pose variations of the head do not confuse the tracker showing that the tracker adapts to novel appearances of the target object.Row3illustrates that only little texture of the object is sufficient for tracking.A glass,having almost no texture,is tracked and again shows the reliability and adaptivity of the proposed tracker.Row4demonstrates the behavior in case of multiple very similar target objects.As can be seen,even though the objects significantly overlap the trackers get not confused demonstrating that the classifiers have really learned to distinguish the specific object from its background.Of course,an overlap over a long duration can cause adaption to the foreground object andfinally leading to failure to track the occluded object because of the bad updates.However,this can be prevented by using some higher level control logic.To summarize,the proposed tracker has the ability to adapt to the appearance of all kinds of objects by learning a good selection of features.Therefore the tracker’s property to adapt is useful for both the handling of appearance variations and for selecting features in order to adapt to any target object.3.2Evaluation on public available sequencesUnfortunately up to now there is no public available framework for comparing tracking techniques.Therefore we decided to process public available sequences,see Figure6and 7,which have already been used in other publications for illustrating tracking results([17, 15]).These sequences contain changes in brightness,view-point and further appearance variations.The results show that the proposed tracker can cope with all these variations and results are at least as good as those presented in the according publications.Figure 5:To illustrate the generality of the proposed method,sequences of four different objects have been captured.The tracking results show that even objects with almost no texture (see row 3)can be successfully tracked.Moreover the tracking algorithm can cope with multiple initialized objects even if they have similar appearance (see row4).(a)Frame30(b)Frame201(c)Frame403(d)Frame451(e)Frame682(f)Frame379(g)Frame451(h)Frame915(i)Frame981(j)Frame 1287Figure 6:These sequences have been provided by Lim and Ross ([17]).The first sequence shows a person moving from dark towards bright area while making changes in pose and and partial occlusion of the target region can be seen.In the second row,an animal doll is moving with large pose,light variations in a cluttered background.8(a)Frame1(b)Frame127(c)Frame269(d)Frame509(e)Frame 885Figure 7:Experimental results on the sequence provided by Jepson [15].Again the object of interest is a face which moves in front of cluttered background and contains variations in appearance.4ConclusionIn this paper we have proposed a robust and generic real-time tracking technique (about 20fps using a standard 1.6GHz PC with 512MB RAM)which considers the tracking problem as a binary classification problem between object and background.Most ex-isting approaches construct a representation of the target object before the tracking task starts and therefore utilize a fixed representation to handle appearance changes during tracking.However,our proposed method does both -adjusting to the variations in ap-pearance during tracking and selecting suitable features which can learn any object and can discriminate it from the surrounding background.The basis is an on-line AdaBoost algorithm which allows to update features of the classifier during tracking.Furthermore the efficient computation of the features allows to use this tracker within real-time appli-cations.Finally,since the tracker is based on a classifier approach now there are several new venues of research like how we can construct a more generic model (like a detector)of the target object during tracking.AcknowledgmentsThe project results have been developed in the MISTRAL Project which is financed by the Austrian Research Promotion Agency (www.ffg.at).In addition this work has been spon-sored in part by the Austrian Federal Ministry of Transport,Innovation and Technology under P-Nr.I2-2-26p VITUS2,by the Austrian Joint Research Project Cognitive Vision under projects S9103-N04and S9104-N04,and by the EU FP6-507752NoE MUSCLE IST project.References[1]S.Avidan.Support vector tracking.PAMI ,26:1064–1072,2004.[2]S.Avidan.Ensemble tracking.In Proc.CVPR ,volume 2,pages 494–501,2005.[3]M.J.Black and A.D.Jepson.Eigentracking:Robust matching and tracking of articulatedobjects using a view-based representation.IJCV ,26(1):63–84,1998.[4] A.Bobick,S.Intille,J.Davis,F.Baird,C.Pinhanez,L.Campbell,Y .Ivanov,A.Schutte,andA.Wilson.The munications of the ACM ,39(3&4):438–455,2000.[5]puter vision face tracking for use in a perceptual user interface.IntelTechnology Journal ,1998.910[6]R.T.Collins,Y.Liu,and M.Leordeanu.Online selection of discriminative tracking features.PAMI,27(10):1631–1643,Oct.2005.[7] aniciu and P.Meer.Mean shift analysis and applications.In Proc.ICCV,volume2,pages1197–1203,1999.[8] aniciu,V.Ramesh,and P.Meer.Real-time tracking of non-rigid objects using meanshift.In Proc.CVPR,volume2,pages142–149,2000.[9]N.Dalal and B.Triggs.Histograms of oriented gradients for human detection.In Proc.CVPR,volume1,pages886–893,2005.[10] A.Elgammal,R.Duraiswami,and L.S.Davis.Probabilistic tracking in joint feature-spatialspaces.In Proc.CVPR,volume1,pages781–788,2003.[11]H.Grabner and H.Bischof.On-line boosting and vision.In Proc.CVPR,volume1,pages260–267,2006.[12]G.D.Hager and P.N.Belhumeur.Efficient region tracking with parametric models of geom-etry and illumination.PAMI,20(10):1025–1039,1998.[13] B.Han and L.Davis.On-line density-based appearance modeling for object tracking.In Proc.ICCV,volume2,pages1492–1499,2005.[14]J.Ho,K.Lee,M.Yang,and D.Kriegman.Visual tracking using learned linear subspaces.InProc.CVPR,volume1,pages782–789,2004.[15] A.D.Jepson,D.J.Fleet,and T.F.El-Maraghi.Robust online appearance models for visualtracking.In Proc.CVPR,volume1,pages415–422,2001.[16]K.Levi and Y.Weiss.Learning object detection from a small number of examples:Theimportance of good features.In Proc.CVPR,pages53–60,2004.[17]J.Lim,D.Ross,R.Lin,and M.Yang.Incremental learning for visual tracking.In Advancesin Neural Information Processing Systems17,pages793–800.MIT Press,2005.[18]Iain Matthews,Takahiro Ishikawa,and Simon Baker.The template update problem.PAMI,26:810–815,2004.[19]H.T.Nguyen and A.Smeulders.Tracking aspects of the foreground against the background.In Proc.ECCV,volume2,pages446–456,2004.[20]T.Ojala,M.Pietik¨a inen,and T.M¨a enp¨a¨a.Multiresolution gray-scale and rotation invarianttexture classification with local binary patterns.PAMI,24(7):971–987,2002.[21] F.Porikli.Integral histogram:A fast way to extract histograms in cartesian spaces.In Proc.CVPR,volume1,pages829–836,2005.[22] D.Ross,J.Lim,and M.Yang.Adaptive proballistic visual tracking with incremental subspaceupdate.In Proc.ECCV,volume2,pages470–482,2004.[23]J.Vermaak,P.P´e rez,M.Gangnet,and A.Blake.Towards improved observation models forvisual tracking:Selective adaption.In Proc.ECCV,pages645–660,2002.[24]P.Viola and M.Jones.Rapid object detection using a boosted cascade of simple features.InProc.CVPR,volume1,pages511–518,2001.[25]P.Viola,M.J.Jones,and D.Snow.Detecting pedestrians using patterns of motion and appear-ance.In Proc.ICCV,volume2,pages734–741,2003.[26]J.Wang,X.Chen,and W.Gao.Online selecting discriminative tracking features using particlefilter.In Proc.CVPR,volume2,pages1037–1042,2005.[27]O.Williams,A.Blake,and R.Cipolla.Sparse bayesian learning for efficient visual tracking.PAMI,27:1292–1304,2005.[28]J.Wu,J.M.Rehg,and M.D.Mullin.Learning a rare event detection cascade by direct featureselection.In Advances in Neural Information Processing Systems16.MIT Press,2003.。

Installation and Configuration Guide for Dual-lens People Counting CameraContentInstallation and Configuration Guide for Dual-lens People Counting Camera (1)Chapter 1. Brief introduction (3)Chapter 2. Installation specification (4)2.1 Focal length and counting width (4)2.2 Accessaries (7)2.3 Installation steps (7)2.4 Counting configuration (12)Configuration for single camera (12)Configuration for multiple cameras (17)Parameter description (17)Chapter 3. Application scenario (21)Chapter 1. Brief introductionDS-2CD6825G0/C-I(V)(S), dual-lens camera, based on the binocular stereo vision technology, adopting 3D head detection and 3D tracking, can obtain accurate real-time trajectory of all moving objects within the monitoring scope, analyze the trajectory data and achieve high-precision people counting. It is very suitable for places where people counting is needed, such as shopping mall, supermarket, chain store, the scenic spot, subway station, bus station, exhibition hall, etc. Meanwhile together with iVMS-4200, platform or server, solutions with comprehensive data analysis and query system can also be established.Chapter 2. Installation specification2.1 Focal length and counting widthThe counting width depends on the installation height. The recommended installation height for dual-lens camera is less than 4.0m (13.1ft). The specific corresponding relation shown below:2.2 Accessaries2.3 Installation stepsPlease pay great attention to the mounting position. Inappropriate mounting position may cause loss of accuracy. The recommended mounting position is shown below:Horizontal sketch map of mounting positionVertical sketch map of mounting positionEffect picture after installationKeep “HIKVISION” logo in the same direction as the “Front” arrow, see picture below:“Front” arrow and “HIKVISION” logo●Make sure camera mounted vertically above the passenger flow(90°vertical by the ground),see picture below:Passenger flow（perpendicular to the door）●For some scenarios where there are multiple cameras, mount the cameras according to tworules.Rule one: If there are turnstiles, each camera should be mounted to monitor certain turnstileRule two: If there is no turnstile, at first, mount the cameras in one line, and make sure the cameras have the same focal length. Then, keep a proper distance between each camera according to the overlap (see the yellow area below) of the adjacent two cameras’ counting width. The suggested overlapping length is between 0.2-0.5m.Sketch map of multiple cameras installationNotice1)People flow should be in vertical up-and-down directionPassenger flow in horizontal direction (wrong)Passenger flow in slant direction (wrong)Passenger flow in vertical up-and-down direction (right)2)Passageway width should be within camera’s counting width3)Avoid obstacles such glass door, shield door and turnstile that block the camera. ForEach divided passageway, mount one camera right above4)The camera should be mounted as close as possible to the passageway withoutobstacle. Remove the obstacle or adjust the camera mounting position if there isobstacle below the camera. Meanwhile, to prevent miscounting, if there’s sliding door onsite, make sure the trajectory of sliding door not overlap the detection line.Otherwise, the counting number may be misled by the door opening and closingToo far away from the door/Obstacle below cameraRecommended installation2.4 Counting configurationConfiguration for single cameraStep 1.S elect VCA Resource. It is necessary to select the VCA source as People Counting Mode.Step 2.R ule Configuration:1)Enter 【VCA】-【People Counting】, check “Enable People Counting”。

多摄像机应用中的视频同步黄飞跃徐光祐清华大学计算机科学与技术系，普适计算教育部重点实验室，北京，100084摘 要：多摄像机应用中的视频同步是一个常用而基本的步骤。

大多数现有方法都依赖于特征点检测和对应，往往复杂而且难以通用。

本文致力于简单视频同步方法的研究，通过对常见同步问题的描述和分析，结合基于多摄像机的动作识别的实际应用场景，提出一套简单易用的视频同步方法。

该方法通过粗框架的运动特征信息来实现对应帧的匹配查找，同时采用统计方法寻求最优帧偏移量。

实验证明，该方法精确度高，具有很好的实用价值。

关键词：视频同步；多摄像机1．引言在计算机视觉尤其是立体视觉的研究和应用工作中，多摄像机的协同工作日益普及。

多摄像机之间的标定[1]和同步是多摄像机协同应用中两个关键的问题。

其中，多摄像机标定是一个比较成熟的问题，然而摄像机间的视频同步则往往被人们忽视。

在实际的多摄像机应用中，摄像机的视频同步基本是一个必须的步骤。

这时候往往不得不采用人工标记和鉴别的方法。

比如通过在多路视频采集时加入特殊标记，比如开关灯、挥手等方法来标记同一帧，然后人工识别和记录各个摄像机之间的偏移量。

当前，就视频同步问题展开的研究不太常见，主要存在以下几种方法：利用双摄像机之间的成像几何约束[2]、利用双摄像机对应点的秩约束[3][4]、利用时空兴趣点的相关性直接同步时间偏移量[5]、整体匹配时空对应点[6]等等。

这些方法大多数都基于多摄像机之间的特征点对应，部分方法甚至从整体上解决了摄像机之间或摄像机内部序列的特征点对应问题。

这些方法为了实现多摄像机间的自动同步，不得不实现精确度要求高、实施困难的对应特征点检测或跟踪，这大大的限制了他们的实际应用价值。

本文详细描述和分析了多摄像机之间的视频同步问题，结合实际的多摄像机应用——基于多摄像机的人体动作识别，提出和实现了一种方便快速的自动同步摄像机视频序列的方法。

这种方法实现简单，结果准确，可扩展性强，具有很好的实用价值。