Research On Human Behavior Detection Method Based On Temporal And Spatial Characteristics

With the development and application of intelligent monitoring technology,human behavior detection technology has become a spot problem in video monitoring area.Using behavior detection technology can accurately detect the behavior of targets in the monitoring area and improve the efficiency of manual screening,timely help relevant personnel deal with emergencies and reduce the losses caused by accidents.However,due to the complex scene background,severe shielding between human bodies,large similarity between behaviors,various behavior categories and other problems,the task of behavior detection becomes difficult.In view of the above problems,the temporal and spatial features(temporal features and spatial features)of video sequence are extracted to detect the behavior of human body in this paper,select typical single-stage YOWO network(You Only Watch Once)and Slow Fast network to research.A reasonable improvement method is proposed to optimize the network structure and improve the accuracy of behavior detection,on the basis of improving the network,a human behavior detection system is built to detect the behavior in the video monitoring scene,analyze the abnormal behavior and store the data.The overall research work is as follows:(1)In order to improve the feature extraction ability and feature fusion ability of single-stage YOWO network,corresponding attention mechanism modules are introduced into spatial flow network and temporal flow network respectively,and the feature fusion module is redesigned with pixel filter,and the Attention-YOWO network is proposed.The results show that under the condition of 16 frame input,the m AP of Attention-YOWO network is improved by 2.8%,up to 31.3%,and the processing speed is up to 16 fps.(2)Aiming at the problems of poor spatial positioning ability and slow processing speed of Attention-YOWO network,further optimize Attention-YOWO network and a new SF-YOWO network is proposed.The network introduces the fast and slow channel mechanism of Slow Fast network,adds horizontal connection,realizes the interaction between spatial information and timing information in the process of feature extraction,and the loss function is modified to improve the stability of boundary box regression.Through the verification of AVA data set and video in monitoring scene,SF-YOWO network achieves 32.1% on m AP,positioning accuracy reaches 85.1% and processing speed reaches 20 fps.(3)According to the algorithm proposed in this paper,designning a human behavior detection system to locate the pedestrian target and identify the behavior in the monitoring screen,and return the detection screen to the display interface for viewing;The function of abnormal behavior detection is realized based on behavior detection.Then,in order to verify the built human behavior detection system,the behavior tracking system based on YOLOv5,Slow Fast and Byte Track trajectory tracking algorithm is compared with the human behavior detection system in this paper,verify that the system constructed in this paper has certain practicability and applicability.The above research and experimental results show that the algorithm proposed in this paper can improve the detection accuracy,meet the application in complex monitoring scenes,and has a certain value for the research in the field of behavior detection.