Summarizing surveillance videos with local-patch-learning-based abnormality detection, blob sequence optimization, and type-based synopsis
Summarizing surveillance videos with local-patch-learning-based abnormality detection, blob sequence optimization, and type-based synopsis
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通过基于局部补丁学习的异常检测、斑点序列优化和基于类型的概要来总结监控视频
DOI：
10.1016/j.neucom.2014.12.044
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发表时间：
2015-05-01
期刊：
影响因子：
6
通讯作者：
Zhou, Yu
中科院分区：
文献类型：
--
作者：
Lin, Weiyao;Zhang, Yihao;Zhou, Yu
在本文中，我们提出了一种新的方法来检测监控视频中的异常活动，并相应地创建合适的摘要视频。该方法首先引入了一种基于补丁的方法来自动建模场景中的正常活动模式和关键区域。通过这种方式，可以有效地从建模的正常模式和关键区域中检测和分类异常活动。然后，提出了一个斑点序列优化过程，该过程综合了空间，时间，大小和运动之间的相关性，以提取合适的前景斑点序列的异常对象。利用该过程，可以有效地避免由于遮挡或背景干扰而导致的斑点提取错误。最后，我们还提出了一种基于异常类型的方法，该方法通过根据其活动类型适当排列异常斑点序列，从长时间输入的监控视频中创建短时间摘要视频。实验结果表明，我们提出的方法可以有效地创建满意的摘要视频从输入的监控视频。(C)2014爱思唯尔有限公司版权所有。
In this paper, we propose a new approach to detect abnormal activities in surveillance videos and create suitable summary videos accordingly. The proposed approach first introduces a patch-based method to automatically model normal activity patterns and key regions in a scene. In this way, abnormal activities can be effectively detected and classified from the modeled normal patterns and key regions. Then, a blob sequence optimization process is proposed which integrates spatial, temporal, size, and motion correlation among objects to extract suitable foreground blob sequences for abnormal objects. With this process, blob extraction errors due to occlusion or background interference can be effectively avoided. Finally, we also propose an abnormality-type-based method which creates short-period summary videos from long-period input surveillance videos by properly arranging abnormal blob sequences according to their activity types. Experimental results show that our proposed approach can effectively create satisfying summary videos from input surveillance videos. (C) 2014 Elsevier B.V. All rights reserved.