| 行人跟踪算法及应用综述 |
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| 引用本文： | 曹自强,赛斌,吕欣.行人跟踪算法及应用综述[J].物理学报,2020(8):154-171. |
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| 作者姓名： | 曹自强 赛斌 吕欣 |
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| 作者单位： | 国防科技大学系统工程学院 |
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| 基金项目： | 国家自然科学基金(批准号:82041020,71771213,91846301,71790615,71901067);湖南省科技计划项目(批准号:2017RS3040,2018JJ1034)资助的课题. |
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| 摘 要： | 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.
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| 关 键 词： | 行人跟踪 轨迹提取 计算机视觉 行人动力学 |
Review of pedestrian tracking:Algorithms and applications |
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| Affiliation: | (College of Systems Engineering,National University of Defense Technology,Changsha 410073,China) |
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| Abstract: | 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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| Keywords: | pedestrian tracking trajectory extraction computer vision human behavioral dynamics |
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