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行人跟踪是计算机视觉领域中研究的热点和难点, 通过对视频资料中行人的跟踪, 可以提取出行人的运动轨迹, 进而分析个体或群体的行为规律. 本文首先对行人跟踪与行人检测问题之间的差别进行了阐述, 其次从传统跟踪算法和基于深度学习的跟踪算法两个方面分别综述了相关算法与技术, 并对经典的行人动力学模型进行了介绍, 最终对行人跟踪在智能监控、拥堵人群分析、异常行为检测等场景的应用进行了系统讲解. 在深度学习浪潮席卷计算机视觉领域的背景下, 行人跟踪领域的研究取得了飞跃式发展, 随着深度学习算法在计算机视觉领域的应用日益成熟, 利用这一工具提取和量化个体和群体的行为模式, 进而对大规模人群行为开展精确、实时的分析成为了该领域的发展趋势.Pedestrian tracking is a hotspot and a difficult topic in computer vision research. Through the tracking of pedestrians in video materials, trajectories can be extracted to support the analysis of individual or collected behavior dynamics. In this review, we first discuss the difference between pedestrian tracking and pedestrian detection. Then we summarize the development of traditional tracking algorithms and deep learning-based tracking algorithms, and introduce classic pedestrian dynamic models. In the end, typical applications, including intelligent monitoring, congestion analysis, and anomaly detection are introduced systematically. With the rising use of big data and deep learning techniques in the area of computer vision, the research on pedestrian tracking has made a leap forward, which can support more accurate, timely extraction of behavior patterns and then to facilitate large-scale dynamic analysis of individual or crowd behavior.
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表 1 卡尔曼滤波的预测阶段和更新阶段
Table 1. Prediction and update process of Kalman filtering
预测阶段 更新阶段 $\hat { x}_k^ - \!=\! { A}\hat { x}_{k-1}^ - \!+\! { B}{ U_{k - 1} }$ ${ { K}_k} \!=\! { P}_k^ - { { H}^{\rm T} }{({ {HP} }_k^ - { { H}^{\rm T} } \!+\! { R})^{ - 1} }$ ${ P}_k^ - \!=\! { A}{ { P}_{k - 1} }{ { A}^T} \!+\! { Q}$ ${ {\hat { x} }_k} \!=\! \hat { x}_k^ - \!+\! { { K}_k}({ { y}_k} \!-\! { H}\hat { x}_k^ - )$ ${{ P}_k} = ({ I} - {{ K}_k}{ H}){ P}$ 
下载: 导出CSV
表 2 卡尔曼滤波公式中的参数及含义
Table 2. Parameters and meanings in the Kalman filter formula
参数 含义 $\hat { x}_k^-$ 目标在$k$时刻的先验状态估计值, 包括目标的位置、速度等参数, 一般是$n$维向量 ${ {\hat { x}}_k}$ 目标在$k$时刻的后验状态估计值, 是对$\hat { x}_k^-$应用卡尔曼滤波更新后的值 ${{\hat { x}}_{k - 1}}$ 目标在$k-1$时刻的后验状态估计值 ${ A}$ 状态转移矩阵, 一般是$n \times n$阶的方阵 ${ B}$ 控制矩阵, 一般为0 ${ U}_{k-1}$ 外部控制量, 一般也为0 ${ P}_k^-$ $k$时刻的先验误差协方差矩阵, 需要事先给定一个初始值, 以后的值可以由卡尔曼滤波递归得到 ${ P}_k$ $k$时刻的后验误差协方差矩阵, 是对${ P}_k^-$的修正 ${ K}_k$ 卡尔曼增益 ${ y}_k$ 测量值, 一般只能测量目标的位置, 是$m$维向量 ${ Q}$ 系统噪声协方差矩阵, 是一个需要调节的参数, 一般假定它是一个固定的值, 在实验中需要通过不断
调节$Q$值, 来寻找滤波器的最优值${ R}$ 观测噪声协方差矩阵, 和测量仪器有关, 在实验中要不断尝试来确定最优的${ R}$值 ${ H}$ 观测矩阵, 是$m \times n$阶矩阵, 用于将$m$维的测量值${ y}_k$转换为与预测值${{\hat { x}}_k}$相同的$n$维向量
下载: 导出CSV
表 3 不同算法之间的性能对比
Table 3. Performance comparison between different algorithms
算法 MOTA↑ MOTP↑ MT↑ ML↓ IDS↓ 数据集 类别 卡尔曼滤波[64] 85.00% — — — — MIT Traffic video dataset 传统跟踪算法 多假设跟踪算法[21] 29.10% 71.70% 12.10% 53.30% 476 MOT Benchmark 传统跟踪算法 粒子滤波算法[27] — — 80.80% 0.70% 10 CAVIAR dateset 传统跟踪算法 基于马尔科夫决策的
多目标跟踪算法[31]30.30% 71.30% 13.00% 38.40% 680 MOT Benchmark 传统跟踪算法 相关滤波算法[65] 83.40% 73.50% — — — Urban Tracker dataset 传统跟踪算法 基于Faster-RCNN的跟踪算法[66] 38.50% 72.60% 8.70% 37.40% 586 MOT 15 Benchmark 深度学习跟踪算法 基于YOLOV3的跟踪算法[67] 60.50% 79.30% 30.20% 19.60% 1129 MOT 16 Benchmark 深度学习跟踪算法
下载: 导出CSV
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[1] Wang C, Sun X, Li H 2019 J. Phys. 1176 032028
[2] 李海翔 2018 硕士学位论文 (合肥: 中国科学技术大学)
Li H X 2018 M.S. Thesis (Hefei: University of Science and Technology of China) (in Chinese)
[3] 黄忠主 2011 硕士学位论文 (长沙: 国防科学技术大学)
Hang Z Z 2011 M.S. Thesis (Changsha: National University of Defense Technology) (in Chinese)
[4] Li X, Hu W, Shen C, Zhang Z, Dick A, Hengel A 2013 ACM Trans. Intell. Syst. Technol. 4 58
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