NDLI: Boost sparse coding based abnormal event detection via explicitly applying temporal continuity constraint

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Boost sparse coding based abnormal event detection via explicitly applying temporal continuity constraint

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Boost sparse coding based abnormal event detection via explicitly applying temporal continuity constraint

Content Provider	ACM Digital Library
Author	Sun, Xiaoshuai Zhao, Sicheng Huang, Yinghao Yao, Hongxun
Abstract	With various novel methods having been proposed, recent years witnessed great progress in abnormal event detection. Broadly speaking, most existing methods can be divided into two categories: global feature representation based ones and local feature representation based ones, though the specific feature model and scale differ a lot. These two types of methods have reverse pros and cons: global feature representation methods can better guarantee spatial-temporal continuity of abnormal events but lack the ability to accurately model features of the basic event elements, while local feature methods are just the opposite. That makes their results complement each other. In this paper, we propose to explicitly apply temporal continuity constraint on sparse coding based local feature representation method, not just enlarging the scale of local feature representation. Experiments demonstrate that our method can usually achieve more stable and smooth results, thus more high detection accuracy. In some cases, the performance gain can be enormous.
Starting Page	1
Ending Page	4
Page Count	4
File Format	PDF
ISBN	9781450335287
DOI	10.1145/2808492.2808521
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2015-08-19
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Temporal continuity constraint Abnormal event detection Sparse coding
Content Type	Text
Resource Type	Article

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