Research And Implementation Of Human Behavior Recognition In Complex Scenes Based On Deep Learning

As an important branch of the field of computer vision,human behavior recognition has been widely concerned by domestic and foreign research scholars.It has important application value and has made important progress in the fields of intelligent monitoring and medical health.With the full application of technology,new problems are constantly emerging.This article studies the problems in which moving human targets in complex scenes are difficult to extract completely,and human behavior features are difficult to accurately characterize.In order to solve the problem of difficult detection of moving targets in complex and changing active scenes,an improved hybrid Gaussian model moving target detection method is constructed:Adaptively adjust the number of Gaussian distribution of the model to improve the utilization efficiency of computing resources;adaptively adjust the model learning rate according to the application scene changes,improve the efficiency of the establishment and update of the background model in complex scenes,and integrate the HSV color space model and the inter-frame difference method,While removing moving shadows,complete detection of moving target areas is achieved.After testing,the detection accuracy and robustness of the improved method have been effectively improved.In order to solve the problem of complex calculation of traditional behavior recognition methods and the difficulty of comprehensively depicting human behavior information,an improved human behavior recognition method based on video stream is proposed:Construct a 2D convolutional neural network based on video framed images for static human behavior recognition;through three-dimensional deep convolution and point-by-point convolution integration solutions of three-dimensional convolution kernels,construct an improved 3D convolutional neural network to extract human behavior The characteristics of time and space dimensions realize the method of human behavior recognition in dynamic scenes.The open source data set and the self-built data set test show that the improved 3D convolutional neural network effectively reduces the consumption of computing resources and effectively improves the accuracy of human behavior recognition in complex scenarios.Combining the above research results,through cross-compilation and multi-thread operation,the design of intelligent monitoring system based on human behavior recognition was completed,and the algorithm test was conducted on the daily dangerous behaviors of elderly people living alone.