An Improved TLD Object Tracking Algorithm

Although TLD (Tracking-Learning-Detection) algorithm can enable the long-term tracking, there are still many problems in it. In this paper, an improvement is made on the detection module of TLD to satisfy the need of time and accuracy. First, we use the Kalman Filter to narrow the detection range of the detector effectively. Then, we replace the traditional detector with Cascaded Random Forest detector, combining the global and local search strategy, which can reduce the computation burden of the algorithm, and achieve the real-time object tracking. The experimental results on various benchmark video sequences show that the proposed approaches compared with the traditional tracking algorithms not only presents robustness and tracking accuracy in stable background or complex conditions, but also obtains the best computing speed with the use of the Cascaded Random Forest.