Multi-complementary features adaptive fusion based on game theory for robust visual object tracking

Abstract

To fully develop the complementary advantages of different visual features and to improve the robustness of multi-feature fusions, we propose a robust correlation filter tracker with adaptive multi-complementary features fusion based on game theory. By combining the complementary features selected from handcrafted features and convolution features, our method constructs two robust combined features in the tracking framework of discriminative correlation filters (DCFs). In addition, by utilizing game theory, the two combined features are regarded as two sides of the game, achieving the best balance through continuous gaming throughout the tracking process and thus obtaining a more robust fused feature. The experimental results obtained on the OTB2015 benchmark dataset demonstrate that our tracker improves the robustness of object tracking in complex scenarios, such as occlusion and deformation, and performs favorably against eight state-of-the-art methods.