What are they doing? : Collective activity classification using spatio-temporal relationship among people
What are they doing? : Collective activity classification using spatio-temporal relationship among people
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DOI：
10.1109/iccvw.2009.5457461
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发表时间：
2009-09
期刊：
影响因子：
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通讯作者：
Wongun Choi;Khuram Shahid;S. Savarese
中科院分区：
文献类型：
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作者：
Wongun Choi;Khuram Shahid;S. Savarese
在本文中，我们提出了一个新的框架，行人行为分类。我们的方法使分类的动作，其语义只能通过观察行人在场景中的集体行为进行分析。这些动作的例子是在街道交叉口等待与排队。为此，我们利用场景中行人的空间分布以及他们的姿势和运动来实现鲁棒的动作分类。我们提出的解决方案采用扩展卡尔曼滤波跟踪检测到的行人在2D 1/2场景坐标，以及摄像机参数和地平线估计跟踪滤波和稳定。我们提出了一个当地的时空描述有效地捕捉行人的空间分布随着时间的推移，以及他们的姿态。该描述符捕获行人活动，同时不需要高水平的场景理解。我们的工作针对低分辨率手持式摄像机捕获的极具挑战性的真实的世界行人视频序列进行了测试。在5类动作数据集上的实验结果表明，我们的解决方案：i）在分类集体行人活动方面是有效的; ii）对具有挑战性的真实的世界条件（如光照，尺度，视点以及部分遮挡和背景运动的变化）是宽容的; iii）优于最先进的动作分类技术。
In this paper we present a new framework for pedestrian action categorization. Our method enables the classification of actions whose semantic can be only analyzed by looking at the collective behavior of pedestrians in the scene. Examples of these actions are waiting by a street intersection versus standing in a queue. To that end, we exploit the spatial distribution of pedestrians in the scene as well as their pose and motion for achieving robust action classification. Our proposed solution employs extended Kalman filtering for tracking of detected pedestrians in 2D 1/2 scene coordinates as well as camera parameter and horizon estimation for tracker filtering and stabilization. We present a local spatio-temporal descriptor effective in capturing the spatial distribution of pedestrians over time as well as their pose. This descriptor captures pedestrian activity while requiring no high level scene understanding. Our work is tested against highly challenging real world pedestrian video sequences captured by low resolution hand held cameras. Experimental results on a 5-class action dataset indicate that our solution: i) is effective in classifying collective pedestrian activities; ii) is tolerant to challenging real world conditions such as variation in illumination, scale, viewpoint as well as partial occlusion and background motion; iii) outperforms state-of-the art action classification techniques.