TLD Visual Target Tracking Algorithm based on Spatio-Temporal Cues

Visual target tracking is one of the hot areas of research in computer vision, and Tracking-Learning-Detection, abbreviated to TLD, is an effective visual tracking framework proposed in recent years. To improve the visual tracking performance in the context of occasional occlusion and time-variable complex background, a spatio-temporal cues based TLD visual target tracking algorithm was proposed in this paper. In the proposed algorithm, hough forests trained by multiple channels features of local image patches were used to detection. The spatial structure of the target object was introduced by multiple sampled local patches to improve the discriminating ability. Then, the initial places of the optical flow tracking were set according to the randomly sampled local patches and the tracking results were weighted by the spatial information. In the end, the output confidences of the optical flow tracking and the output confidences of the detections of the hough forests were fused in an adaptive spatio-temporal way. Experimental results demonstrate advantages of the proposed method in handling occlusion and tracking in complex environment, compared with the original TLD.