人体运动异常行为监测

Monitoring of the Abnormal Behavior in Human Actions

针对人体运动行为监测中离线学习开销大的缺点,提出了一种实时在线学习的多因子权值评价方式。在轨迹中心差值分布、形体倾斜角度和形体高宽比例等3个权值因子的基础上,通过这3个指标进行加权平均,获得行为量化指数Action,根据Action的值对人体行为进行判断:当取值大于2正常,低于1.5预警,小于1异常。实验结果表明,这种方式在实时性上表现优越,并克服了开销大的缺点。

The cost of monitoring human actions is very high through off-line learning. The authors propose a system to solve the problem. The system has the capacity of real-time online learning and monitoring human actions. And the system consists of three weight factors, distribution of trajectory center difference, body tilt angle and ratio of height versus width. The three indexes are weighted and averaged, the quantitative index for behavior action is obtained, then the human behavior is judged according to the value of action ： when the values greater than 2, it is considered normal, less than 1.5, the warning is needed while less than 1 it is considered abnormal. The result of experiment shows that this approach is superior in performance on real-time and the shortwoming of the high cost is overcome.