Human body abnormal behavior detection method based on T-TINY-YOLO network

Abstract

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.

Description

technical field [0001] The invention relates to a T-TINY-YOLO network-based human body abnormal behavior detection method, which belongs to the technical field of image and video analysis and processing in computer vision. Background technique [0002] With the improvement of imaging equipment and the strengthening of public traffic safety awareness, people's demand for video detection and abnormal situation detection has also increased. Video detection has been widely used in various fields, such as civil anti-theft, atmospheric observation, Disaster monitoring, enemy reconnaissance, agriculture, forestry and vegetation protection, etc., which also put forward higher requirements for the processing method, speed and application level of abnormal target detection. To detect objects in video streams, it is necessary to clarify the relationship and differences between judging abnormal object detection and ordinary object detection. [0003] Traditional target detection and re...

AI Technical Summary

Problems solved by technology

[0009] Aiming at the complex and diverse detection problems of abnormal human behaviors in the existing surveillance video, the present invention proposes a detection method for abnormal human behaviors based on T-TINY-YOLO network. Improve the abnormal target detection technology, tailor the network around the real-time aspect; at the same time, transplant it to the embedded hardware platform TX2 and run it successfully, and get accurate extraction results

Images