The invention provides a video human body abnormal behavior detection method based on skeleton point trajectory dynamic analysis, which comprises the following steps: step 1) uniformly dividing an input video to obtain a plurality of video sequences; 2) performing human skeleton point track extraction on each frame of image in the video sequence; and 3) inputting the human skeleton point trajectory into a time domain hole convolutional neural network for feature extraction and behavior classification. The method provided by the invention is based on skeleton point trajectory analysis, and compared with a traditional method based on an RGB image or an optical flow mode, the skeleton point trajectory can directly reflect the motion mode of a behavior subject, so that the interference of background motion is reduced; furthermore, compared with the traditional dense convolutional network, the hole convolutional network model provided by the invention has the advantages of small model parameter quantity, small operand and contribution to real-time detection. On a currently published test data set, the method provided by the invention obtains a leading detection level.
Video human body abnormal behavior detection method based on skeleton point trajectory dynamic analysis