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Homography Based Multiple Camera Detection and Tracking of People in a Dense Crowd

机译:基于茂密的人群中的多种摄像头检测和跟踪人们

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Tracking people in a dense crowd is a challenging problem for a single camera tracker due to occlusions and extensive motion that make human segmentation difficult. In this paper we suggest a method for simultaneously tracking all the people in a densely crowded scene using a set of cameras with overlapping fields of view. To overcome occlusions, the cameras are placed at a high elevation and only people's heads are tracked. Head detection is still difficult since each foreground region may consist of multiple subjects. By combining data from several views, height information is extracted and used for head segmentation. The head tops, which are regarded as 2D patches at various heights, are detected by applying intensity correlation to aligned frames from the different cameras. The detected head tops are then tracked using common assumptions on motion direction and velocity. The method was tested on sequences in indoor and outdoor environments under challenging illumination conditions. It was successful in tracking up to 21 people walking in a small area (2.5 people per m{sup}2), in spite of severe and persistent occlusions.
机译:在密集的人群跟踪的人是一台摄像机跟踪器由于闭塞和广泛的运动,使人体难以分割一个具有挑战性的问题。在本文中,我们建议使用具有重叠视野的相机同时跟踪密集拥挤的场景中的所有人员的方法。为了克服闭塞,相机放置在高度高度,只有人们的头部被跟踪。由于每个前景区域可以由多个受试者组成,头部检测仍然困难。通过组合来自多个视图的数据,提取高度信息并用于头部分割。通过施加强度相关性来检测被视为在各种高度处的2D贴片的头部顶部,以从不同的摄像机对准帧。然后使用关于运动方向和速度的常见假设来跟踪检测到的头部顶部。在挑战性照明条件下对室内和室外环境的序列进行测试。尽管严重和持续的闭塞,它成功地追踪了最多21人在一个小区域行走(每M {Sup} 2)。

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