Document Detail

Infrared human tracking with improved mean shift algorithm based on multicue fusion.
MedLine Citation:
PMID:  19623234     Owner:  NLM     Status:  MEDLINE    
Mean shift has been presented as a well known and efficient algorithm for tracking infrared targets. However, under complex backgrounds, such as clutter, varying illumination, and occlusion, the traditional mean tracking method often converges to a local maximum and loses the real infrared target. To cope with these problems, an improved mean shift tracking algorithm based on multicue fusion is proposed. According to the characteristics of the human in infrared images, the algorithm first extracts the gray and edge cues, and then uses the motion information to guide the two cues to obtain improved motion-guided gray and edge cues that are fused adaptively into the mean shift framework. Finally an automatic model update is used to improve the tracking performance further. The experimental results show that, compared with the traditional mean shift algorithm, the presented method greatly improves the accuracy and effectiveness of infrared human tracking under complex scenes, and the tracking results are satisfactory.
Xin Wang; Lei Liu; Zhenmin Tang
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Publication Detail:
Type:  Evaluation Studies; Journal Article; Research Support, Non-U.S. Gov't    
Journal Detail:
Title:  Applied optics     Volume:  48     ISSN:  1539-4522     ISO Abbreviation:  Appl Opt     Publication Date:  2009 Jul 
Date Detail:
Created Date:  2009-07-22     Completed Date:  2009-10-08     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  0247660     Medline TA:  Appl Opt     Country:  United States    
Other Details:
Languages:  eng     Pagination:  4201-12     Citation Subset:  IM    
School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing, Jiangsu 210094, China.
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MeSH Terms
Image Enhancement / methods
Image Interpretation, Computer-Assisted / methods*
Imaging, Three-Dimensional / methods*
Infrared Rays
Pattern Recognition, Automated / methods*
Reproducibility of Results
Sensitivity and Specificity
Subtraction Technique*
Whole Body Imaging / methods*

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine

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