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

Clicks: 272
ID: 110389
2012
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Star

Ranked #847 of 1,694 articles by views in sensors

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 1,694 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
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*

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.