Object Tracking Based on the Combination of Learning and Cascade Particle Filter

The problem of object tracking in dense clutter is a challenge in computer vision. This paper proposes a method for tracking object robustly by combining the online selection of discriminative color features and the offline selection of discriminative Haar features. Furthermore, the cascade particle filter which has four stages of importance sampling is used to fuse two kinds of features efficiently. When the illumination changes dramatically, the Haar features selected offline play a major role. When the object is occluded, or its rotation angle is very large, the color features selected online play a major role. The experimental results show that the proposed method performs well under the conditions of illumination change, occlusion, object scale change and abrupt motion of object or camera.