基于加权样本选择与主动学习的视频异常行为检测算法<sup>*</sup>

doi:10.16451/j.cnki.issn1003-6059.201604006

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Abstract Due to the surge in public security issues, anomaly detection in video surveillance is a hot topic in computer vision. Taking characteristics of the dataset for video anomaly detection into account, a video anomaly detection algorithm based on weighted sample selection and active learning is proposed. According to the distribution characteristics of the dataset, appropriate weights for instances are selected to remove the effect of imbalanced data on the classifier. Active learning is used to select the uncertain instances. To adapt to the complex environment, the model is updated iteratively. Experimental results on UCSD dataset show that the proposed algorithm outperforms traditional algorithms.

Key words： Video Surveillance Anomaly Detection Active Learning

Received: 28 April 2015

ZTFLH:

TP 391

Fund:Supported by National Natural Science Foundation of China (No.61432008,61305068,61321491,61363066), Graduate Research Innovation Program of Jiangsu Province (No.CXZZ13_0055)

About author:: (LIN Ling， born in 1975， Ph.D. candidate. Her research interests include image processing and machine learning.)
(LIAO De， born in 1992， master student. His research inte-rests include data mining and machine learning.)
(GAO Yang(Corresponding author)， born in 1973， Ph.D.， professor. His research interests include big data， pattern recognition and machine learning.)
(YANG Wanqi， born in 1988. Ph.D. candidate. Her research interests include image processing and machine learning.)

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	LIN Ling
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	YANG Wanqi

Cite this article:

LIN Ling,LIAO De,GAO Yang等. Video Anomaly Detection Algorithm Based on Weighted Sample Selection and Active Learning[J]. , 2016, 29(4): 341-349.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.201604006 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2016/V29/I4/341

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