Compressive Tracking Based on Online Hough Forest

Existing tracking algorithms often suffer from the drift and lost problems caused by factors such as pose variation, illumination change, occlusion and motion. Therefore, developing a robust and effective tracker is still a challenging task. In this paper, we propose a real-time compressive tracking based on online Hough forest. The gray and texture features of discrete samples are extracted and compressed via the random measurement matrix. Online Hough forest classifier is used to vote the location probability of the target, and it optimizes the confidence map estimation for the target detection. The location of target being tracked is determined by combining the upper frame of the target center location and the probability confidence map of the incremental Hough forest. Finally, the classifier parameters are updated online by introducing the illumination variation and target occlusion feedback mechanism adaptively. The experiments with state-of-the-art algorithms on challenging sequences demonstrated that the proposed algorithm can effectively enhance the robustness and accuracy, and inherit the real-time performance of the compressive tracking algorithm.