Tracking objects using shape context matching
In this paper we propose a novel tracking method, which provides accurately segmented object boundaries. The first step of the proposed method is to model the object and background using Gaussian mixture model (GMM), and extract a rough contour according to the object edge features. And then the states of the object, including translation, rotation and scale, are estimated using shape context matching. Finally, we take an elastic shape matching method to extract the exact contour. The proposed method is robust enough for tracking object with translation, rotation, scale change and partial occlusion, and it can also be used for real-time tracking applications. Experiments on both synthetic and real world video sequences demonstrate the effectiveness of the proposed method.
shape context gaussian mixture model object tracking shape prior