Multi-feature Selection Correlated Filtering Target Tracking Algorithms Based on Location-Scale Different Space Coordination

Aiming at the problem that the traditional correlation filtering tracking algorithm is easy to fail when the target is occluded and scale variation occurs, this paper proposed a multi-feature selection correlation filtering target tracking algorithm based on position-scale spatial coordination. Firstly, the features of fast histogram of oriented gradient, color names and average grey value of the region are extracted then different features are fused to form feature pools for correlation filtering tracking to enhance the discriminant capability of tracker,. Secondly, according to the robustness score of each feature response, the maximum value of response map with the highest score is selected to predict the target location. Then, the target scale is estimated using phase correlation filter in log-polar coordinates. Finally, updating filter template with the strategy of high confidence model updating. The experimental results on standard datasets of TB-50 and OTB-2015 prove that the proposed algorithm performs effectively under the circumstances of scale variation, occlusion, out-of-plane rotation, out-of-view and background clutters.