title: 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.
authors.affiliation: School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing, Jiangsu 210094, China. rongtian_helen@yahoo.com.cn
authors.forename: Xin
authors.initials: X
authors.lastname: Wang
authors.forename: Lei
authors.initials: L
authors.lastname: Liu
authors.forename: Zhenmin
authors.initials: Z
authors.lastname: Tang
mesh_terms: D000465:Algorithms; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D007259:Infrared Rays; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique; D051598:Whole Body Imaging / Q000379:methods*