Infrared human tracking with improved mean shift algorithm based on multicue fusion

Abstract

Mean shift has been presented as a well known and efficient algorithm for tracking infrared targets. However, under complex backgrounds, such as clutter, varying illumination, and occlusion, the traditional mean tracking method often converges to a local maximum and loses the real infrared target. To cope with these problems, an improved mean shift tracking algorithm based on multicue fusion is proposed. According to the characteristics of the human in infrared images, the algorithm first extracts the gray and edge cues, and then uses the motion information to guide the two cues to obtain improved motion-guided gray and edge cues that are fused adaptively into the mean shift framework. Finally an automatic model update is used to improve the tracking performance further. The experimental results show that, compared with the traditional mean shift algorithm, the presented method greatly improves the accuracy and effectiveness of infrared human tracking under complex scenes, and the tracking results are satisfactory.