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Deep representation for abnormal event detection in crowded scenes
Feng, Yachuang1,2; Yuan, Yuan1; Lu, Xiaoqiang1
2016-10-01
会议名称24th ACM Multimedia Conference, MM 2016
会议录名称MM 2016 - Proceedings of the 2016 ACM Multimedia Conference
页码591-595
会议日期2016-10-15
会议地点Amsterdam, United kingdom
出版者Association for Computing Machinery, Inc
产权排序1
摘要

Abnormal event detection is extremely important, especially for video surveillance. Nowadays, many detectors have been proposed based on hand-crafted features. However, it remains challenging to effectively distinguish abnormal events from normal ones. This paper proposes a deep representation based algorithm which extracts features in an unsupervised fashion. Specially, appearance, texture, and short-term motion features are automatically learned and fused with stacked denoising autoencoders. Subsequently, long-term temporal clues are modeled with a long short-term memory (LSTM) recurrent network, in order to discover meaningful regularities of video events. The abnormal events are identified as samples which disobey these regularities. Moreover, this paper proposes a spatial anomaly detection strategy via manifold ranking, aiming at excluding false alarms. Experiments and comparisons on real world datasets show that the proposed algorithm outper-forms state of the arts for the abnormal event detection problem in crowded scenes. © 2016 ACM.

关键词Motion Analysis
学科领域Data Processing And Image Processing
作者部门光学影像学习与分析中心
DOI10.1145/2964284.2967290
收录类别EI ; ISTP
ISBN号9781450336031
语种英语
引用统计
文献类型会议论文
条目标识符http://ir.opt.ac.cn/handle/181661/28439
专题光学影像学习与分析中心
作者单位1.Center for OPTical IMagery Analysis and Learning (OPTIMAL), State Key Laboratory of Transient Optics and Photonics, Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an, Shaanxi, 710119, China
2.University of Chinese Academy of Sciences, Beijing, 100049, China
推荐引用方式
GB/T 7714
Feng, Yachuang,Yuan, Yuan,Lu, Xiaoqiang. Deep representation for abnormal event detection in crowded scenes[C]:Association for Computing Machinery, Inc,2016:591-595.
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