Human Behavior Analysis System Design And Realization Based On Spatiotemporal Local Pattern Coding

The technology of human behavior recognition involves the research subjects in the fields of technology, such as image processing, pattern recognition, artificial intelligence and computer vision. This recognition technology has broad application prospect in real life, including the fields of virtual reality, tracking and analysis of actions, video security monitoring and posture correction in exercise training. In past researches, research objects were often limited to human actions in normal color videos and posture images. As the3D somatosensory technology matures gradually, including the launch of3D somatosensory devices Kinect and Leap Motion, human actions are recognized through3D videos instead of2D videos. In this paper, acquire video of human actions through Kinect somatosensory device, analyze and recognize human actions from the aspect of theoretical algorithm, and then achieve recognition of human actions and system platform of personalized control service. The main research jobs in this paper include:Firstly, introduce and analyze common methods of human action recognition, verify their effect via simulation experiment and find out the deficiencies, and then introduce the method of human action recognition discussed in this paper. In this paper, for color videos and videos with depth of fields, extract objects of human actions from video sequence via background subtraction, model color videos and depth videos via models of time domain,motion and historical contour and3D spatiotemporal model, then extract valid image of human actions from videos and lay the foundation for extraction of feature information of human actions from video sequence.Then, in terms of feature information extraction of spatiotemporal local pattern coding, this paper introduces local binary patterns (LBP) and textural features with various expanded forms, and extract features of images from models of time domain, motion and historical contour and changing images from3D spatiotemporal model, that is, acquire data of textural features of3D spatiotemporal local pattern to classify these features. In terms of feature classification, this paper adopts K-Nearest Neighbor, nearest neighbors based on K-means algorithm, and Hidden Markov Model to classify textural features of3D spatiotemporal local pattern and recognize actions.Finally, on the basis of theoretical algorithm and experimental verification, apply the method proposed in this paper to system platform software according to situations of real life and achieve personalized control service of smart household control, motion simulation in street and panoramic image browsing. Meanwhile, introduce CUDA, a kind of parallel processing technology, to achieve quick processing of action videos and recognition of actions.