Learn to Track Edges

Reliability of a model-based edge tracker critically depends on its ability to establish correct correspondences between points on the model edges and edge pixels in an image. This is a non-trivial problem especially in the presence of large inter-frame motions and in cluttered environments. We propose an online learning approach to solving this problem. An edge pixel is represented by a descriptor composed of a small segment of intensity patterns. From training examples the algorithm utilizes the randomized forest model to learn a posteriori distribution of correspondence given the descriptor. In a new frame, the edge pixels are classified using maximum a posteriori (MAP) estimation. The proposed method is very powerful and it enables us to apply the proposed tracker to many previously impossible scenarios with unprecedented robustness.