Fast object tracking with global kernel density seeking

An object tracking algorithm with global kernel density seeking is proposed to avoid local probability mode in mean shift tracking process. Firstly, a monotonically decreasing sequence of bandwidths is obtained according to the object scale. At the first bandwidth, a maximum probability can be found with mean shift, and the next iteration loop started at the previous convergence location. Finally, the best density mode is obtained at the optimal bandwidth. In the convergence process, with the smoothness effect of the large bandwidth, the compact of the local probability mode is avoided, and the precise position of the object can be found with the optimal bandwidth, which is similar to the object scale. To speed up the convergence, Over-Relaxed strategy is introduced to enlarge the step size. Under the convergence rule, the correlation coefficient is used to adopt the learning rate. The experimental results prove that the proposed tracker with global kernel density seeking is robust in high-speed object tracking, and performs well in occlusions. The adaptive Over-Relaxed strategy is effective to lower the convergence iterations by enlarging the step size.