Research And Application Of Human Body Behavior Recognition Algorithm For Intelligent Security Scenes

With the continuous advancement of the construction of smart city and the rapid development of artificial intelligence technology,intelligent security and intelligent management of personnel have begun to receive widespread attention from the society.Human body behavior recognition is the core link in mining personnel information through video surveillance,which is of great significance to the construction of smart city.However,in the face of complex surveillance scenes and massive amounts of video data,traditional human body behavior recognition methods can no longer meet the increasing demands of industrial applications.This paper is oriented to intelligent security scenes,computer vision technology based on deep learning,combined with deep convolutional neural network theory,research on human body behavior recognition algorithms in multiple complex surveillance scenarios,and specific industrial applications of the algorithms,then we proposed a new behavior recognition solution.The following are the main research of this article:(1)Aiming at the problem that behavior recognition algorithms are easily affected by complex monitoring scenes,based on public scenes and security scenes,we established 7 diversified and complex scene data sets,involving 50 common human body behaviors to enhance the robustness of the algorithm.Firstly,we performed data annotation preprocessing according to the human body behavior categories in the image.Secondly,we used clustering algorithm to analyze the characteristics of the human body target in the data set,and combined the anchor matching strategy to obtain the anchor parameters of the human body target.Finally,according to the distribution characteristics of the data,we studied different data enhancement strategies to improve the generalization ability of the model.(2)Aiming at the problems of low detection accuracy and efficiency in existing human body detection algorithms,this paper studied respectively the detection network and backbone network of the algorithm,and proposed two improved detection algorithms.Firstly,based on the feature image pyramid and receptive field theory,we deeply studied the feature fusion method of the detection network and the generation strategy of feature maps,and proposed a human body detection algorithm RFIP,which effectively improved the accuracy of human detection.Secondly,based on the RFIP algorithm,multi-scale receptive field and residual network construction ideas,we designed a backbone network DetNet-49 that meets the characteristics of human body detection,and proposed another human body detection algorithm MSRD.Compared with the RFIP algorithm,the MSRD algorithm can not only guarantee a certain detection efficiency,but also further improve the accuracy of human body detection.(3)Aiming at the problems of low recognition accuracy and efficiency in existing human body behavior classification algorithms,this paper studied respectively the structure optimization of the network and the lightweight of the model,and proposes two improved classification algorithms.Firstly,we studied the impact of the receptive field and the number of channels of the classification network on the performance of the algorithm,and proposed the DRAMNet algorithm based on the design idea of the deep residual attention mechanism and the multi-branch convolution structure,which improved the classification accuracy of the model.Secondly,we inspired by the idea of lightweight network construction with deep supervision,and introduced deep separable hole convolutions,designed a lightweight and efficient behavior classification algorithm LebNet,which improved the recognition efficiency of the model.(4)Finally,for intelligent security scenes,based on human body detection and behavior classification algorithms,we designed a human body behavior recognition solution that takes into account detection accuracy and efficiency,and applied it to the task of detecting staff sleeping on the job,which can realize real-time detection and automatic early warning of human body abnormal behaviors,and effectively improve the level of intelligent management of corporate employees.At the meantime,we considered the extended application of the behavior recognition algorithms and solution proposed in this article in more industrial scenes.