Confidence map based KCF object tracking algorithm

Tracking with kernelized correlation filters (KCF) is an excellent object tracking algorithm, which is widely concerned. In KCF, each candidate patch in tracked region corresponds to a confidence ratio indicating the probability of containing the target, and the patch with the maximum confidence ratio is the output. In traditional KCF, its tracking performance decreases in complex scenes and the model is liable to be contaminated due to updated every frame. To overcome these limitations, we combine all available confidence ratios to form a confidence map, then by analyzing the confidence map, we infer the tracking scene and adopt different tracking strategies. For complex scenes, we dynamically improve KCF to enhance its tracking performance. In addition, we propose an innovative model update mechanism to reduce the computational complexity and model contamination. The experimental results show that compared with the conventional KCF algorithm, the proposed approach improves success rate and precision by 7% and 8% respectively.