Video Anomaly Detection Algorithm Based on Weighted Sample Selection and Active Learning
LIN Ling1,2,3, LIAO De4, GAO Yang1,3, YANG Wanqi1,3
1.State Key Laboratory for Novel Software Technology, Department of Computer Science and Technology,Nanjing University, Nanjing 210023 2.School of Electronics and Information Engineering, Yili Normal University, Yining 835000 3.Collaborative Innovation Center of Novel Software Technology and Industrialization, Nanjing University,Nanjing 210023 4.Department of Computer Science, University of Illinois, Urbana-Champaign 61801
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.
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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2016, Vol. 29 
