中文摘要：

为提高在线学习目标跟踪的实时性和准确率,结合压缩感知理论,提出一种将距离度量学习（DML）运用到目标跟踪的算法。首先,根据所选定的目标位置分别提取目标和背景样本集,运用随机投影理论对样本的Harr-like特征进行压缩;然后,用压缩后的低维特征向量集训练度量矩阵;最后,在新的一帧中抽取目标和背景的样本,用训练得到的度量矩阵计算已知目标和样本间的Mahalanobis距离,距离最小的样本的位置就是所要跟踪的目标的位置。对不同视频序列的测试结果表明,用压缩特征表示目标,使特征计算的计算量压缩到原来的1/4,减少了特征计算的时间;用训练后的度量矩阵计算目标位置,即跟踪器能够根据目标的不断变化自适应调整参数,提高了跟踪的准确率。

英文摘要：

To improve the precision and real-time quality of on-line learning object tracking, combined with the compress sensing theory, an algorithm using distance metric learning is proposed. First, target samples and background samples around the selected target are sampled. The Harr-like feature vectors are compressed using the random projection theory. Then,the distance metric is trained using the com- pressed feature vectors. Finally, the Mahalanobis distance between the samples in the new coming frame and the known target is calculated. The location of the sample closest to the known target is the location wanted. Experiments on variant videos show that the caculating load of the compressed features is 3/4 less than that using the uncompressed ones. Calculating the location of target using the trained distance metric makes the tracking precision higher.