Research on semi-supervising learning algorithm for target model updating in target tracking

Target detection and tracking technique is one of the hot subjects in image processing and computer vision fields, which has significant research value not only in military areas such as imaging guidance and military target tracking, but also for civil use such as security and monitoring and the intelligent man-machine interaction. In this paper, for target deformation, scale changing, rotation, and other issues in the long-term stable target tracking, a bootstrapping feedback learning algorithm is proposed, which may improve the target model and the classifier discriminating capacity as well as the fault tolerance ability; and it also makes fewer errors during the updating, and then the proof of convergence of the algorithm is given. Experimental results show that among the same tracking algorithms, utilization of the learning method to update the target model and classifier is more stable and more adaptable than unusing it in the processes of target scale changing, deformation, rotation, perspective changing and fuzzy. And compared with the existing conventional method, this method has a better robustness, and a high value in practical application and research.