Robust Kernel-Based Tracking with Multiple Subtemplates in Vision Guidance System

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ID: 110389
2012
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Abstract
The mean shift algorithm has achieved considerable success in target tracking due to its simplicity and robustness. However, the lack of spatial information may result in its failure to get high tracking precision. This might be even worse when the target is scale variant and the sequences are gray-levels. This paper presents a novel multiple subtemplates based tracking algorithm for the terminal guidance application. By applying a separate tracker to each subtemplate, it can handle more complicated situations such as rotation, scaling, and partial coverage of the target. The innovations include: (1) an optimal subtemplates selection algorithm is designed, which ensures that the selected subtemplates maximally represent the information of the entire template while having the least mutual redundancy; (2) based on the serial tracking results and the spatial constraint prior to those subtemplates, a Gaussian weighted voting method is proposed to locate the target center; (3) the optimal scale factor is determined by maximizing the voting results among the scale searching layers, which avoids the complicated threshold setting problem. Experiments on some videos with static scenes show that the proposed method greatly improves the tracking accuracy compared to the original mean shift algorithm.
Reference Key
yan2012sensorsrobust Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Yuzhuang Yan;Xinsheng Huang;Wanying Xu;Lurong Shen;Yan, Yuzhuang;Huang, Xinsheng;Xu, Wanying;Shen, Lurong;
Journal sensors
Year 2012
DOI
10.3390/s120201990
URL
https://www.mdpi.com/1424-8220/12/2/1990##related_content
Keywords
voting tracking mean shift subtemplate vision guidance National Center for Biotechnology Information NCBI NLM MEDLINE pubmed abstract nih national institutes of health national library of medicine computer-assisted / methods* pattern recognition automated / methods* pmid:22438749 pmc3304151 doi:10.3390/s120201990 yuzhuang yan xinsheng huang lurong shen algorithms* artificial intelligence* image enhancement / methods* image interpretation robotics / methods*

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