Hand Gesture Target Model Updating and Result Forecasting Algorithm based on Mean Shift

To propose a gesture model updating and results forecasting algorithm based on Mean Shift, and to solve the problem of target model changing and influenced tracking results in gesture target tracking process. Firstly, the background difference and skin color detection methods are used to detect and get gesture model, and the Mean Shift algorithm is used to track gesture and update the gesture model, and finally to use the Kalman algorithm to predict the gesture tracking results. The experimental results show that this algorithm reduces the influence of surrounding environment in gesture tracking process, and get better tracking result.