Human Abnormal Behavior Detection Method Based on Improved YOLOv3 Network Model

Abstract

The data of traditional video surveillance is very large and complex, and cannot detect the abnormal behaviors of human in a timely and effective manner.In response to these problems, this paper presents an improved YOLOv3 algorithm (YOLOv3-MSSE) for the detection of human abnormal behavior.This algorithm can improve the detection accuracy of large targets for it is based on the traditional YOLOv3 network model, and a multi-scale feature extraction network is constructed by the residual mo-dules.At the same time, by incorporating the attention mechanism into different positions of the network structure, and the importance of the features in each channel of the feature map can be weighted, which effectively improves the detection performance of the model for abnormal human behavior.Experimental results show that compared with the traditional YOLOv3 algorithm, the mAP of YOLOv3-MSSE is increased by 20.8%, and F1-scores is increased by 11.3%.The proposed algorithm can not only detect the specific abnormal behavior of the human in the monitoring scene effectively, but also can balance the relationship between the detection accuracy rate and the recall rate well.In addition, It is more suitable for the detection of human abnormal behavior in actual monitoring scenarios than similar methods.

Keywords

• neural networks|abnormal behavior|multiscale|attention mechanism|resnet