基于图流挖掘的视频人群活动变化点检测

Meng Yang, Lida Rashidi, S. Rajasegarar, C. Leckie, A. S. Rao, M. Palaniswami
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引用次数: 4

摘要

近年来,人们对检测视频中的异常行为模式越来越感兴趣。在这项工作中,我们提出了一种新的活动变化点检测方法来识别视频监控中的人群运动异常,从而解决了这个问题。在我们提出的新框架中,利用超球面聚类算法自动识别感兴趣区域,然后监测每对感兴趣区域在连续时间间隔内的行人流量密度,并将其表示为邻接矩阵序列,其中通过有向图捕获流量的方向和密度。最后,我们使用图编辑距离和累积和测试来检测图序列中的变化点。我们在四个真实世界的视频数据集上进行实验:都柏林、新奥尔良、阿比路和MCG数据集。我们观察到,对于这些数据集,我们提出的方法实现了高f度量,即在[0.7,1]范围内。结果表明,该方法可以在全局和局部水平上成功地检测到所有数据集的变化点。我们的结果也证明了我们提出的算法在变化点检测和分割任务中的效率和有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Crowd Activity Change Point Detection in Videos via Graph Stream Mining
In recent years, there has been a growing interest in detecting anomalous behavioral patterns in video. In this work, we address this task by proposing a novel activity change point detection method to identify crowd movement anomalies for video surveillance. In our proposed novel framework, a hyperspherical clustering algorithm is utilized for the automatic identification of interesting regions, then the density of pedestrian flows between every pair of interesting regions over consecutive time intervals is monitored and represented as a sequence of adjacency matrices where the direction and density of flows are captured through a directed graph. Finally, we use graph edit distance as well as a cumulative sum test to detect change points in the graph sequence. We conduct experiments on four real-world video datasets: Dublin, New Orleans, Abbey Road and MCG Datasets. We observe that our proposed approach achieves a high F-measure, i.e., in the range [0.7, 1], for these datasets. The evaluation reveals that our proposed method can successfully detect the change points in all datasets at both global and local levels. Our results also demonstrate the efficiency and effectiveness of our proposed algorithm for change point detection and segmentation tasks.
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