Research On Hand Tracking Method Based On Particle Filter And Active Contour Model

With the continuous development of computer hardware and software and virtual reality technology,people also increasingly hope that the man-machine interaction mode can develop from the computer-centered input mode to the people-oriented mode to form a more interactive form that meets the human communication mode.In this case,the tracking and recognition of human hands instead of the mouse and keyboard as the main exchange mode has become a new generation of interactive trends.Combined with human skin color information and contour information,this paper realizes the position tracking and contour tracking of human hand under complicated background and large-scale occlusion,and provides important data information for gesture recognition.First of all,this paper analyzes the various characteristics of the human hand movement and the many problems caused by these characteristics.In order to solve these non-Gaussian and nonlinear problems,Bayesian estimation is proposed to solve the problem.However,due to the limitation of the traditional Bayesian estimation algorithm,the approximate substitution used in the Bayesian estimation algorithm will lead to the error accumulation,resulting in the failure of tracking.Therefore,the particle filter algorithm based on Monte Carlo estimation is introduced instead of calculating the posterior probability density distribution of prediction target.A particle filter algorithm based on color histogram is proposed to realize human hand tracking.In a simple background,this paper tracks the handwriting process and verify the accuracy and effectiveness of the particle filter algorithm.Secondly,the particle filter algorithm cannot describe the specific details of the target,is prone to generate error accumulation of particle model,resulting in drift occurs.The supervision and error correction mechanism is introduced to achieve real-time updates the particle model.The GVF Snake based on gradient vector flow is introduced to realize the static acquisition of the real contour of hand and to ensure the true position of the predicted position and to correct the parameters of the particle filter model.At the same time,the background of human hand movement is more complicated and has more interference.The skin color elliptic clustering model is introduced.The method of enhancing the gray level of skin color is proposed to enhance the human hand gradient information and weaken the background interference.In order to improve the veracity and quickness of human hand contour convergence,the self-adaptive external guiding force and adaptive gradient vector field are introduced,and a skin color adaptive GVF Snake model is proposed.After experimental verification,the model can be achieved in a complex background,quickly and accurately converging to the exact location of human hand.Finally,the improved GVF Snake model is combined with particle filter to realize human hand tracking.After experimental comparison and verification,this proposed algorithm can improve the tracking accuracy of human hand under complicated background and large-scale occlusion.