中文摘要：

针对分类器学习常常面临高维数据的问题，借助稀疏表示理论对目标样本多尺度Harr特征进行数据降维，构建朴素贝叶斯分类器进行目标正负样本的学习和更新，选择具有最大分类器响应值的样本作为目标的当前状态，实现了对运动目标的快速而有效的跟踪。实验结果表明该方法适用于机器人运动目标跟踪，在提高实时性的同时能保持一定的鲁棒性。

英文摘要：

For the problem of high-dimensional data faced with classifier learning, this paper proposes a method of data dimensionality reduction to multi-scale Harr featrues with sparse representation theory, and chooses Naive Bayes classifier to learn and update the positive and negative samples of target. The sample with the maximum classification response value is selected as the current status of the target, that achieves fast and efficient tracking of moving target. Experimental resuhs show that the method improves the real-time while maintaining a certain robustness and can apply to moving target tracking of visual robot.