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An on-line learning tracking of non-rigid target combining multipleinstance boosting and level set
Chen, Mingming1,2; Cai, Jingju1
Volume8918
Pages891802
2013
Language英语
ISSN0277786X
DOI10.1117/12.2031170
Indexed ByEi
Subtype会议论文
AbstractVisual tracking algorithms based on online boosting generally use a rectangular bounding box to represent the position of the target, while actually the shape of the target is always irregular. This will cause the classifier to learn the features of the non-target parts in the rectangle region, thereby the performance of the classifier is reduced, and drift would happen. To avoid the limitations of the bounding-box, we propose a novel tracking-by-detection algorithm involving the level set segmentation, which ensures the classifier only learn the features of the real target area in the tracking box. Because the shape of the target only changes a little between two adjacent frames and the current level set algorithm can avoid the re-initialization of the signed distance function, it only takes a few iterations to converge to the position of the target contour in the next frame. We also make some improvement on the level set energy function so that the zero level set would have less possible to converge to the false contour. In addition, we use gradient boost to improve the original multi-instance learning (MIL) algorithm like the WMILtracker, which greatly speed up the tracker. Our algorithm outperforms the original MILtracker both on speed and precision. Compared with the WMILtracker, our algorithm runs at a almost same speed, but we can avoid the drift caused by background learning, so the precision is better. © 2013 SPIE.; Visual tracking algorithms based on online boosting generally use a rectangular bounding box to represent the position of the target, while actually the shape of the target is always irregular. This will cause the classifier to learn the features of the non-target parts in the rectangle region, thereby the performance of the classifier is reduced, and drift would happen. To avoid the limitations of the bounding-box, we propose a novel tracking-by-detection algorithm involving the level set segmentation, which ensures the classifier only learn the features of the real target area in the tracking box. Because the shape of the target only changes a little between two adjacent frames and the current level set algorithm can avoid the re-initialization of the signed distance function, it only takes a few iterations to converge to the position of the target contour in the next frame. We also make some improvement on the level set energy function so that the zero level set would have less possible to converge to the false contour. In addition, we use gradient boost to improve the original multi-instance learning (MIL) algorithm like the WMILtracker, which greatly speed up the tracker. Our algorithm outperforms the original MILtracker both on speed and precision. Compared with the WMILtracker, our algorithm runs at a almost same speed, but we can avoid the drift caused by background learning, so the precision is better. © 2013 SPIE.
Conference NameProceedings of SPIE: MIPPR 2013: Automatic Target Recognition and Navigation
Conference Date2013
Citation statistics
Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type会议论文
Identifierhttp://ir.ioe.ac.cn/handle/181551/7700
Collection光电探测与信号处理研究室(五室)
Affiliation1. Institute of Optics and Electronics, Chinese Academy of Sciences, Chengdu, Sichuan Province, 610209, China
2. Graduate University of Chinese Academy of Sciences, Beijing, 100049, China
Recommended Citation
GB/T 7714
Chen, Mingming,Cai, Jingju. An on-line learning tracking of non-rigid target combining multipleinstance boosting and level set[C],2013:891802.
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