The invention discloses a
human body abnormal behavior detection method based on a T-TINY-YOLO network, and belongs to the technical field of image and video analysis and
processing in
computer vision. The method comprises the following specific steps: firstly, according to different monitoring scenes, selecting a
video sequence within a period of time, converting the
video sequence into a picture, storing and preprocessing the picture; labeling four human abnormal behaviors which comprises finger, push, hug and standing in each picture by using a labeling tool, and generating an
xml file as adata set; then, dividing the
data set into a training sample and a
verification sample, and inputting the training sample and the
verification sample into an improved T-TINY-YOLO
network model for training and
verification; and finally, for a new monitoring video frame picture, directly inputting the new monitoring video frame picture into the trained T-TINY-YOLO
network model after preprocessing, and outputting the category to which the
calibration result of the human abnormal behavior belongs, thereby realizing end-to-end abnormal behavior classification. According to the invention, the size of the network is
cut, the problem of
network redundancy is solved, and the
algorithm is accelerated, so that the network detection and time utilization are more effective.