摘要
针对传统行为识别技术实时性、鲁棒性较差等问题,提出了一种高效鲁棒性的人体行为识别算法。通过基于Meanshift和Kalman滤波相结合的跟踪算法来跟踪定位人体目标;利用肢体特征和区域特征来提取运动特征;利用基于OAA的支持向量机分类识别。仿真实验表明,该算法实时性好、鲁棒性高,能有效应用于监控系统中。
In view of the traditional behavior recognition technology＇s problem of poor real-time performance and robustness, this paper proposes a kind of efficient and robust human behavior recognition algorithm. Meanshift and Kalman is used when the human behavior is tracked real-timely, and selected area of regional and joint angles of limbs to represent human movement, on the target classification step, the OAA-SVM is set up. Simulation experiments show that this method has better robustness and the real-time performance, and can be effectively to the monitoring system.
出处
《网络安全技术与应用》
2014年第6期23-24,26,共3页
Network Security Technology & Application
关键词
行为识别
目标跟踪
特征提取
监控系统
action recognition
target tracking
feature extraction
monitoring system
