基于区域划分与标准时间的手部异常行为检测

Abnormal hand behavior detection during operation based on intelligent video surveillance systems can prevent human errors and improve human reliability. In order to solve the problems that the motion characteristics of hand operation are not obvious, and common abnormal detection and gesture recognition methods are not applicable, a detection technology of abnormal hand behavior based on area division and standard time is proposed. The skin color detection method based on ellipse model was used to detect the hand centroids in the video. The work area division method is proposed to define the unit task. The continuous operation was divided into unit tasks according to the work area of the hand centroid in each frame, and the start and end time and the duration of each unit task were obtained. Standard time was defined by normal working hours. And warnings were given to the unit tasks which exceed the standard time range. Experimental results show that the accuracy rate of unit task segmentation is higher than 93%, and the detection rate of abnormal behavior is higher than 86%. The proposed method can effectively detect abnormal hand behavior and provide technical support for human error monitoring and early warning.