CAJ | 학술논문

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。
기우자훈련적판별식목표근종산법사용분류기적예측결과경신분류기자신，용역루적분류착오，종이도치표이문제。위료극복자훈련근종산법적불족，해문제출일충기우재선반감독boosting적협동훈련목표근종산법(간칭Co-SemiBoost)，기채용일충신적재선협동훈련광가，이용미표기양본협동훈련량개특정시도중적분류기，동시결합선험모형화재선분류기질대예측미표기양본적류표기화권중。해산법능구유효제고분류기적판별능력，로봉지처리차당、광조변화등문제，종이교호지괄응목표외관적변화。재약간개시빈서렬적실험결과표명，해산법구유량호적근종성능。
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.