A new TLD target tracking method based on improved correlation filter and adaptive scale

Target tracking is a popular but challenging problem in computer vision field. Due to many disturbing factors such as position transformation, illumination, and occlusion, it is difficult to achieve continuous target tracking. On the basis of the above analyses, a novel target tracking method based on correlation filters is proposed in this paper. This method uses the improved Tracking–Learning–Detection (TLD) tracking framework which combines the tracker with the detector through the learning mechanism. In the TLD tracking framework, the Spatially Regularized Discriminatively Correlation Filters tracker is used and improved. In addition, the adaptive tracking scale is realized according to the confidence of the searching area. The experimental results show that the proposed algorithm can effectively deal with the attitude change and the illumination problem so that it has better robustness and stability for target continuous tracking.