Automatic Detection and Tracking of Pedestrians in Videos with Various Crowd Densities
Automatic Detection and Tracking of Pedestrians in Videos with Various Crowd Densities
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DOI：
10.1007/978-3-319-02447-9_1
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
Afshin Dehghan;Haroon Idrees;Amir Zamir;M. Shah
中科院分区：
文献类型：
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作者：
Afshin Dehghan;Haroon Idrees;Amir Zamir;M. Shah
对于大量的监控视频数据集，手动分析行人和人群通常是不切实际的。自动跟踪人体是对此类视频进行计算机分析的基本能力之一。在这篇主题论文中，我们提出了两种最先进的方法，用于在低人群密度和高人群密度的视频中进行自动行人跟踪。对于低密度的视频，首先我们使用基于部分的人体检测器检测每个人。然后，我们采用了一种基于广义图的全局数据关联方法来跟踪整个视频中的每个个体。在高人群密度的视频中，我们使用场景结构化力模型和人群流建模来跟踪个体。此外，我们提出了一种替代方法，利用上下文信息，而不需要学习的场景结构。执行的评估表明，所提出的方法优于目前可用的算法在几个基准。
Manual analysis of pedestrians and crowds is often impractical for massive datasets of surveillance videos. Automatic tracking of humans is one of the essential abilities for computerized analysis of such videos. In this keynote paper, we present two state of the art methods for automatic pedestrian tracking in videos with low and high crowd density. For videos with low density, first we detect each person using a part-based human detector. Then, we employ a global data association method based on Generalized Graphs for tracking each individual in the whole video. In videos with high crowd-density, we track individuals using a scene structured force model and crowd flow modeling. Additionally, we present an alternative approach which utilizes contextual information without the need to learn the structure of the scene. Performed evaluations show the presented methods outperform the currently available algorithms on several benchmarks.