基于在线半监督boosting的协同训练目标跟踪算法

基于自训练的判别式目标跟踪算法使用分类器的预测结果更新分类器自身，容易累积分类错误，从而导致漂移问题。为了克服自训练跟踪算法的不足，该文提出一种基于在线半监督boosting的协同训练目标跟踪算法(简称Co-SemiBoost)，其采用一种新的在线协同训练框架，利用未标记样本协同训练两个特征视图中的分类器，同时结合先验模型和在线分类器迭代预测未标记样本的类标记和权重。该算法能够有效提高分类器的判别能力，鲁棒地处理遮挡、光照变化等问题，从而较好地适应目标外观的变化。在若干个视频序列的实验结果表明，该算法具有良好的跟踪性能。

A Novel Co-training Object Tracking Algorithm Based on Online Semi-supervised Boosting

The self-training based discriminative tracking methods use the classification results to update the classifier itself. However, these methods easily suffer from the drifting issue because the classification errors are accumulated during tracking. To overcome the disadvantages of self-training based tracking methods, a novel co-training tracking algorithm, termed Co-SemiBoost, is proposed based on online semi-supervised boosting. The proposed algorithm employs a new online co-training framework, where unlabeled samples are used to collaboratively train the classifiers respectively built on two feature views. Moreover, the pseudo-labels and weights of unlabeled samples are iteratively predicted by combining the decisions of a prior model and an online classifier. The proposed algorithm can effectively improve the discriminative ability of the classifier, and is robust to occlusions, illumination changes, etc. Thus the algorithm can better adapt to object appearance changes. Experimental results on several challenging video sequences show that the proposed algorithm achieves promising tracking performance.