中文摘要：

为了解决目标在复杂环境下表观变化引起的跟踪漂移问题,提出一种基于多特征融合与分类器在线学习的目标跟踪算法.该算法利用不同表观特征训练子分类器,通过构建损失函数求得各子分类器可信度,进而加权融合子预测结果,得到当前帧最佳目标状态估计;同时,依据最近-最远边界原则和协同训练理论粗更新训练样本集,并通过精选择准则得到更具代表性的训练样本集,实现子分类器自适应更新.实验结果表明,所提出的算法在多种典型测试场景中都能取得较鲁棒的跟踪效果.

英文摘要：

To solve the tracking drift problem caused by object appearance change in complex environments, the paper proposes an object tracking algorithm on the basis of multi-feature fusion and classifier online learning. The algorithm trains the sub-classifier with different apparent features, and calculates the reliability of each classifier by building the loss function, and then the optimum target state estimation by means of the weighted fusion prediction results of each sub- classifier is obtained. Meanwhile, it updates the training sample set coarsely according to the nearest-farthest boundary principle as well as the co-training theory, and gets more representative ones with the refined selection criterion, which further updates the sub-classifier adaptively. Experimental evaluations demonstrate that the proposed algorithm achieves favorable tracking performance against state-of-the-art methods on various typical testing scenarios.