1.南京大学 计算机科学技术系 计算机软件新技术国家重点实验室 南京 210023 2.伊犁师范学院 电子与信息工程学院 伊宁 835000 3.南京大学 软件新技术与产业化协同创新中心 南京 210023 4.Department of Computer Science, University of Illinois, Urbana-Champaign 61801
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.
林玲，廖德，高阳，杨琬琪. 基于加权样本选择与主动学习的视频异常行为检测算法*[J]. 模式识别与人工智能, 2016, 29(4): 341-349.
LIN Ling, LIAO De, GAO Yang, YANG Wanqi. Video Anomaly Detection Algorithm Based on Weighted Sample Selection and Active Learning. , 2016, 29(4): 341-349.
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2016, Vol. 29 
