Object Tracking with Mean Shift and Model Prediction

In this paper, a method for real-time tracking of moving targets is proposed. The particle filter and mean shift technical for color-based tracking is used. The traditional tracker always focuses on how to track with the object robustly in a short period of time. Most of them modify the model after the tracking is finished in current frame. But in long time tracking, the object model is changing continuously. Under the mean shift tracking framework, particle filter technical is used to predict the object model, and track with the new one. With this method, we don´t need to fix a threshold to modify the model manually. The experimental results show that out methods has better performance than the traditional kernel based tracker.