Boost sparse coding based abnormal event detection via explicitly applying temporal continuity constraint
Article No.: 29, Pages 1 - 4
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.
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Index Terms
- Boost sparse coding based abnormal event detection via explicitly applying temporal continuity constraint
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Published In
August 2015
397 pages
ISBN:9781450335287
DOI:10.1145/2808492
- General Chairs:
- Ramesh Jain,
- Shuqiang Jiang,
- Program Chairs:
- John Smith,
- Jitao Sang,
- Guohui Li
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 19 August 2015
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- Research-article
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- National Natural Science Foundation of China
- National Science Foundation of China, Key Program
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ICIMCS '15
ICIMCS '15: International Conference on Internet Multimedia Computing and Service
August 19 - 21, 2015
Hunan, Zhangjiajie, China
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ICIMCS '15 Paper Acceptance Rate 20 of 128 submissions, 16%;
Overall Acceptance Rate 163 of 456 submissions, 36%
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