计算机工程与应用2017，Vol.53Issue(8)：165-169,185,6.DOI:10.3778/j.issn.1002-8331.1510-0065
基于改进的稀疏重构算法的行人异常行为分析
Pedestrian abnormal behavior analysis based on optimized sparse reconstruction algorithm
摘要
Abstract
In order to identify abnormal behavior in the video surveillance, first of all, it tracks the pedestrian, and then analyzes the trajectory to determine whether there is abnormal behavior. In the pedestrian tracking, the Kalman filter and spatial-temporal context algorithm are combined together, which can effectively avoid the shelter problem in complicated background. In the analysis of abnormal behavior, the trajectory is classified according to the shape to get the normal trajectory scenario set. It analyzes the trajectory by optimized sparse reconstruction algorithm and distinguishes normal or abnormal according to the reconstruction residual. The experimental results show that the proposed method has higher recognition rate compared with the original method.关键词
视频监控序列/目标跟踪/时空上下文/异常分析/稀疏重构算法Key words
video monitoring sequence/target tracking/spatial-temporal context/abnormal analysis/sparse reconstruction algorithm分类
信息技术与安全科学引用本文复制引用
汤春明,卢永伟..基于改进的稀疏重构算法的行人异常行为分析[J].计算机工程与应用,2017,53(8):165-169,185,6.基金项目
天津市第三批三年千人计划项目(No.62014511) （No.62014511）
天津工业大学引进教师科研启动项目(No.030367). （No.030367）
