Multi-camera activity correlation analysis
Multi-camera activity correlation analysis
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DOI：
10.1109/cvpr.2009.5206827
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发表时间：
2009-06
期刊：
影响因子：
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通讯作者：
Chen Change Loy;T. Xiang;S. Gong
中科院分区：
文献类型：
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作者：
Chen Change Loy;T. Xiang;S. Gong
我们提出了一种新的方法来建模活动之间的相关性在一个忙碌公共空间捕获的多个非重叠和未校准的相机。在我们的方法中，每个摄像机视图被自动分解成语义区域，跨不同的时空活动模式进行观察。一种新的交叉典型相关分析（xCCA）框架制定检测和量化的时间和因果关系的区域活动内和跨相机视图。该方法完成了三个任务：（1）估计相机网络的空间和时间拓扑结构;（2）促进更鲁棒和准确的人员重新识别;（3）通过链接跨相机视图收集的视觉证据来执行全局活动建模和视频时间分割。我们的方法与现有技术的不同之处在于，它不依赖于摄像机内或摄像机间的跟踪。因此，它可以应用于即使是最具挑战性的视频监控设置具有严重的闭塞和极低的空间和时间分辨率。它的有效性证明了使用153小时的视频从8个摄像头安装在一个繁忙的忙碌地铁站。
We propose a novel approach for modelling correlations between activities in a busy public space captured by multiple non-overlapping and uncalibrated cameras. In our approach, each camera view is automatically decomposed into semantic regions, across which different spatio-temporal activity patterns are observed. A novel Cross Canonical Correlation Analysis (xCCA) framework is formulated to detect and quantify temporal and causal relationships between regional activities within and across camera views. The approach accomplishes three tasks: (1) estimate the spatial and temporal topology of the camera network; (2) facilitate more robust and accurate person re-identification; (3) perform global activity modelling and video temporal segmentation by linking visual evidence collected across camera views. Our approach differs from the state of the art in that it does not rely on either intra or inter camera tracking. It therefore can be applied to even the most challenging video surveillance settings featured with severe occlusions and extremely low spatial and temporal resolutions. Its effectiveness is demonstrated using 153 hours of videos from 8 cameras installed in a busy underground station.