关键词: 目标跟踪, 在线学习, 半监督学习, 目标漂移, 结构约束

Abstract: When the tracked objects get seriously obscured or have fast moving, the self-training method based on online semi-supervised boosting will lead to the error accumulation thus easily suffering from the drifting issue or even tracking failure. To overcome the disadvantages, a novel object tracking algorithm is proposed based on online semi-supervised boosting with positive（P） and negative（N） constraints, termed PN-SemiT. The proposed algorithm trains a binary classifier by using online semi-supervised boosting algorithm, and the training process is guided by structural constraints which restrict the labeling of the unlabeled set. Therefore, P-N constraints can evaluate the classifier, identify examples that have been classified in contradiction with structural constraints and adjust the?classifier?error?in?real-time. Experimental results on several different challenging video sequences show that the proposed algorithm has a superior tracking performance, and can alleviate the object drifting problem under complex environments.

Key words: object tracking, online learning, semi-supervised learning, object drifting, structural constraints