Infrared Target Tracking Based on Adaptive Multiple Features Fusion and Mean Shift

For target tracking by using single feature results in a poor performance in robustness, an infrared object tracking method based on adaptive multi-features fusion and Mean Shift (MS) is presented. In order to enhance the important features, the proposed method advances local contrast mean difference characteristic and uses advanced local contrast mean difference characteristic and grey features to present the interested target. Uncertainty measurement method is introduced in features fusion to adjust the relative contributions of different features adaptively, and the robustness of MS algorithm is significantly enhanced. Furthermore, scale operator is introduced to update tracking window in order to improve the tracking performance in size-changing target. Experimental results indicate the proposed method is more robust to present object and has good performance in complex scene.