The Body Detection And Tracking Based On The Vision

The detection and tracking of human body is an important problem in the computer vision field. Its application has spread intelligent monitoring, sports analysis, a new human computer interaction and virtual reality and other fields. Researching the technology of the human detection and tracking based on vision has very important practical significance. Body detection in the smart home environment and human motion tracking based on video have been studied in this dissertation. The main work and characteristic are as fol-lows:1. The key technologies of human movement image analysis were reviewed.This paper gives detail steps in the human motion image which contain the initialization model, human tracking, pose estimation and behavior recognition, the major research methods, the progress and problems.2. Research an improved adaptive Gaussian mixture model for human detection algo-rithm.The frequent changes in the home background need to be described by the mixed Gaussian function modeling. Existing adaptive Gaussian mixture models can quickly model the background, but when the body stays a short time at a position, they will learn it for the background. S-Curve function which is an approximate piecewise exponential function is used to modify the background learning rate and the new Gaussian component weights. After that, a combination of morphology, median filtering and image Pyramid Technology, is used to cuts off human shadow and reduces the background noise.3. Research an automatic human tracking algorithm by the fusion of Gaussian mixture model and particle swarm optimization.The target foreground area given by Gaussian mixture background model is used to narrow the range of human tracking. The random search capability of particle swarm op-timization method in the state space is used to find the optimal spatial location of the body. Finally, the information fusion between them can achieve automatic human tracking al-gorithm. The algorithm framework maintains a history of histogram information for each tracking object, and can automatically recover tracking and identify targets in the later image frame when self-occlusion or human target leaving the scene.