Learning deep event models for crowd anomaly detection

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• 作者：Yachuang Feng^a ; ^bAuthor Vitae ; Yuan Yuan^aAuthor Vitae ; Xiaoqiang Lu^a ; ^{luxq666666@gmail.com}Author Vitae
• 关键词：Deep neural network ; PCANet ; Deep GMM ; Crowded scene ; Abnormal event detection ; Video surveillance
• 刊名：Neurocomputing
• 出版年：2017
• 出版时间：5 January 2017
• 年：2017
• 卷：219
• 期：Complete
• 页码：548-556
• 全文大小：1012 K
• 卷排序：219

文摘

Abnormal event detection in video surveillance is extremely important, especially for crowded scenes. In recent years, many algorithms have been proposed based on hand-crafted features. However, it still remains challenging to decide which kind of feature is suitable for a specific situation. In addition, it is hard and time-consuming to design an effective descriptor. In this paper, video events are automatically represented and modeled in unsupervised fashions. Specifically, appearance and motion features are simultaneously extracted using a PCANet from 3D gradients. In order to model event patterns, a deep Gaussian mixture model (GMM) is constructed with observed normal events. The deep GMM is a scalable deep generative model which stacks multiple GMM-layers on top of each other. As a result, the proposed method acquires competitive performance with relatively few parameters. In the testing phase, the likelihood is calculated to judge whether a video event is abnormal or not. In this paper, the proposed method is verified on two publicly available datasets and compared with state-of-the-art algorithms. Experimental results show that the deep model is effective for abnormal event detection in video surveillance.