Real-time tracking of infrared dim-small target with multi-feature adaptive fusion under double confidence

In order to improve the robustness and real-time performance of the algorithm, a series of problems, such as ground background clutter, similar target interference and weak targets appear in the process of infrared dim small target tracking. Kalman filter is first used to predict the initial position of the target, and the initial position is set as the center of the region of interest (ROI). Next, determine the filter response graph by extracting the target’s local binary (LBP) features, gradient (HOG) features, and grayscale (GRAY) features within the ROI region. Fusion weights are obtained according to the average peak correlation energy (APCE) of the three response results and the consistent frame response (CFR) of the adjacent frames, and the response results of the three features are fused by adaptive weighted fusion. To estimate the optimal location of the target. Finally, the target model is updated and the target position is taken as the measure of the Kalman filter. According to experimental data, the average distance precision (DP), overlap precision (OP), and real-time tracking speed for infrared ground background image sequences in various scenarios are 0.782, 0.731, and 94.7 frames per second, respectively. The algorithm can effectively improve the accuracy and robustness of tracking in complex environments.