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On-line distance metric learning tracking using compressed feature
Chen D.-C.; Zhu M.; He B.-G.; Yang W.-B.
2014
发表期刊Guangdianzi Jiguang/Journal of Optoelectronics Laser
ISSNISBN/10050086
卷号25期号:8页码:1592-1597
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 compressed 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 calculating 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.
收录类别EI
语种中文
文献类型期刊论文
条目标识符http://ir.ciomp.ac.cn/handle/181722/44207
专题中科院长春光机所知识产出
推荐引用方式
GB/T 7714
Chen D.-C.,Zhu M.,He B.-G.,et al. On-line distance metric learning tracking using compressed feature[J]. Guangdianzi Jiguang/Journal of Optoelectronics Laser,2014,25(8):1592-1597.
APA Chen D.-C.,Zhu M.,He B.-G.,&Yang W.-B..(2014).On-line distance metric learning tracking using compressed feature.Guangdianzi Jiguang/Journal of Optoelectronics Laser,25(8),1592-1597.
MLA Chen D.-C.,et al."On-line distance metric learning tracking using compressed feature".Guangdianzi Jiguang/Journal of Optoelectronics Laser 25.8(2014):1592-1597.
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