BAYESIAN VISUAL TRACKING WITH EXISTENCE PROCESS

计算机工程与设计COMPUTER ENGINEERING AND DESIGN

2021年12月

第42卷第12

期

Dec. 2021Vol. 42

No. 12

结合多注意力机制的自监督目标跟踪张志远，

杨帆+

(中国民航大学计算机科学与技术学院，天津

300300)

摘 要：为解决现有目标跟踪数据集不足及手工标注数据成本过大的问题，提出结合判别式相关滤波及多注意力机制的自 监督目标跟踪方法。训练集选用原始未标记的视频图像，使用子空间注意力机制及通道注意力机制针对不同的输入目标对 特征进行自适应调整，构建相关滤波输出响应图进行跟踪定位；

通过前向跟踪和后向跟踪两个跟踪过程，以自监督的训练

方式用最终响应结果与初始标签构建循环一致性损失。在OTB50和OTB100两个公开数据集的实验结果表明了所提方法的

实时性和有效性。

关键词：

目标跟踪；相关滤波；自监督学习；注意力机制；循环一致性损失

中图法分类号：TP391 文献标识号：

A

文章编号：1000-7024 (2021) 12-3502-08

doi： 10.16208/j.

issnl000-7024.

2021.12. 027

Self-supervised object tracking based on

multi-attention mechanism

ZHANG Zhi-yuan, YANG Fan

+

(School of Computer Science and Technology, Civil Aviation University

of China, Tianjin 300300, China)

Abstract： To solve the problem of insufficient target tracking data set and excessive

manual labeling

data

cost, a self-supervised

target tracking method combining discriminant

algorithms that keep only one configuration state [5], which are therefore sensitive to single failures in the presence of ambiguities or fast or erratic motion. In this paper, we address two important issues related to tracking with a particle filter. The first issue refers to the specific form of the observation likelihood, that relies on the conditional independence of observations given the state sequence. The second one refers to the choice of an appropriate proposal distribution, which, unlike the prior dynamical model, should take into account the new observations. To handle these issues, we propose a new particle filter tracking method based on visual motion. Our method relies on a new graphical model allowing for the natural introduction of implicit or explicit motion information in the likelihood term, and on the exploitation of explicit motion measurements in the proposal distribution. the above issues, our approach, and their benefits, is given in the following paragraphs. The definition of the observation likelihood distribution is perhaps the most important element in visual tracking with a particle filter. This distribution allows for the evaluation of the likelihood of the current observation given the current object state, and relies on the specific object representation. The object representation corresponds to all the information that characterizes the object like the target position, geometry, appearance, color, etc. Parametrized shapes like splines [2] or ellipses [6], and color distributions [5]–[8], are often used as target representation. One drawback of these generic representations is that they can be quite unspecific, which augments the chances of ambiguities. One way to improve the robustness of a tracker consists of combining low-level measurements such as shape and color [6]. The generic conditional form of the likelihood term relies on a standard hypothesis in probabilistic visual tracking, namely the independence of observations given the state sequence [2], [6], [9]–[13]. In this paper, we argue that this assumption can be inaccurate in the case of visual tracking. As a remedy, we propose a new model that assumes that the current observation depends on the current and previous object configurations as well as on the past observation. We show that under this more general assumption, the obtained particle filtering algorithm has similar equations than the algorithm based on the standard hypothesis. To our knowledge, this has not been shown before, and so it represents the first contribution of this article. The new assumption can thus be used to naturally introduce implicit or explicit motion information in the observation likelihood term. The introduction of such data correlation between successive images will turn generic trackers like shape or color histogram trackers into more specifi in Model-Based Stochastic Tracking

学校代码***** 学号************ 分类号TP391 密级公开硕士学位论文基于稀疏表示的目标跟踪算法研究学位申请人邵豪指导教师张莹副教授学院名称信息工程学院学科专业控制科学与工程研究方向机器视觉与智能信息处理二○一八年六月四日Research on Target Tracking Algorithm Based on Sparse RepresentationCandidate Shao HaoSupervisor Associate Prof. Zhang YingCollege College of Information EngineeringProgram Control Science and EngineeringSpecialization Machine vision and intelligent information processing Degree Master of EngineeringUniversity Xiangtan UniversityDate Jun, 2018摘要目标跟踪就是在连续的视频序列中，建立所要跟踪物体的位置关系，得到物体完整的运动轨迹，目前广泛应用于军事导航、城市智能交通管控、视频监控以及人机交互等方面。

在目标运动的过程中，会出现姿态或形状的变化、尺度的变化、背景遮挡或光线亮度的变化等情况，降低了跟踪算法的准确率，构建一个性能高效、稳定的适应强的追踪算法是当前跟踪领域的一大难题。

为解决目标跟踪过程中出现的目标丢失及提高跟踪效率，本文做了以下研究：（1）为提高计算速率，减小背景信息的干扰并增强稀疏表示跟踪模型性能，提出了一种利用分段加权函数构建的反向稀疏跟踪算法，将跟踪问题转化为在贝叶斯框架下寻找概率最高的候选对象问题，通过构造不同的分段权重函数分别度量候选目标与正负模板的判别特征系数。

利用池化降低跟踪结果的不确定性干扰，选择正模板与负模板相减得到的最大差值系数所对应的候选表示作为当前跟踪结果。

of
gaze using as input a sequence of people images. In [12] are employed Kalman filtering and Mean shift tracking. This proposed eye tracker has the capacity to deal with variable lighting conditions, occlusion, glasses and it is prepared to track multiple efficiency. Some authors employ probabilistic approaches to develop robust eye trackers. In [11], Kalman filter is used to monitor and analyze the movements of the eyes from a set of real-time images. A discrete Kalman filter is developed for the recursive estimation of the eye regions. Its application allows encompass the system information and the measurement noise in its dynamics model, and deal with signals that change with time. The eye tracking system is fast on the control of the eye movements, automated and it needs small computational requirements. Recently, a particle filters approach is applied in [4]. The proposed eye tracking method uses particle filters due to their capacity of defming multi-modal distributions and robustness. On our approach, particle filters are also applied, but we realize the tracking of the two eyes of the human face. On the other hand, a different approach is proposed in Martin et al. [8]. They demonstrate a deformable template method to solve the problem of eye tracking that deals with rapid eye movements. Their experiences prove that the proposed model is fast and effective even when the images present low resolution. In this paper, we propose an eye tracking system based on the application of particle filters. Our system is divided in two parts: eye detection and eye tracking. For eye detection we apply cascaded Haar classifiers in order to fmd the eye regions in a human face. In our method we use the physical properties of pupils and their dynamics to fmd the eye regions in a human face. The eye tracking is implemented through the use of particle filters that are robust in situations of variable light conditions and require small computational resources when compared with other methods. Our eye tracking system can detect and track the human eyes when the face appears in the field view of the camera without any type of marks in the human face or specific light conditions. The obtained results demonstrate the robustness of our architecture even when the conditions are unfavorable. The developed eye tracking system is robust even in presence of variable lighting conditions and is very fast in the detection of the eye regions in the human face. Furthermore, in our contribution we do not require any camera calibration, pre­ registration of the users and the eye tracking system works for all people. The proposed system also detects the eyes of more than one person in a real-time video frame without restrictions. people at the same time without lost of