Activity Perception for Smart Video Surveillance Sytems

摘要：

This paper presents a novel framework for activities perception in video surveillance scenarios. Firstly, moving objects are detected by modeling the background using Gaussian Mixture Model (GMM). Secondly, a novel adaptive particle filter (APF) is introduced. The proposed APF has time-varying dimensions and can track multiple moving objects entering or leaving the field of view effectively. Finally, object trajectories are classified by predefined rules for activity analysis. Experimental results demonstrate the robustness and effectiveness of our method.

关键词： activity perception background modeling adaptive particle filter motion trajectories

作者: Dong Xia Hao Sun Jun Guo Zhenkang Shen

作者单位: School of Electrical Science and Engineering National University of Defense Technology Changsha, China

会议类型: 国际会议

会议名称: The 2010 International Conference on Computer Application and System Modeling(2010计算机应用与系统建模国际会议 ICCASM 2010)

会议地点: 太原

会议语种:英文

页码: 15-18

在线出版日期: 2010-10-22（万方平台首次上网日期，不代表论文的发表时间）