Kernel correlation filter tracking strategy based on adaptive fusion response map

Aiming at the problem that the tracking performance of the traditional kernel correlation filter tracking algorithm is easy to be affected by illumination variation, occlusion and motion blur during tracking, an improved tracking strategy is proposed. A new Histogram of Hue Gradient (HHG) feature is designed, and the new HOG-HHG feature is obtained by connecting the HOG and the HHG in series. Two features, CN and HOG-HHG, are extracted respectively, and two kernel correlation filter classifiers are constructed base on the two features above to establish the corresponding response maps of the tracking scenes, respectively. The response maps are fused adaptively to improve the tracking robustness to the complex situations in the tracking process. The updating strategy of the target model is designed based on peak sidelobe ratio (PSR) and its difference, and the adaptive thresholds are used to improve the stability of the target model. Simulation results show that the proposed method has better tracking adaptability to the illumination variation, occlusion and motion blur. Both the precision and the success rate can be enhanced.