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Computational and Mathematical Methods in Medicine
Volume 2013 (2013), Article ID 925341, 8 pages
Research Article

The Research and Application of Visual Saliency and Adaptive Support Vector Machine in Target Tracking Field

1School of Computer Science and Engineering, Nanjing University of Science & Technology, Nanjing 210094, China
2School of Computer and Communication Engineering, Changsha University of Science & Technology, Changsha 410114, China
3Department of Computer Science and Technology, Hunan Vocational Institute of Safety & Technology, Changsha 410151, China

Received 15 September 2013; Accepted 4 November 2013

Academic Editor: Sabri Arik

Copyright © 2013 Yuantao Chen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The efficient target tracking algorithm researches have become current research focus of intelligent robots. The main problems of target tracking process in mobile robot face environmental uncertainty. They are very difficult to estimate the target states, illumination change, target shape changes, complex backgrounds, and other factors and all affect the occlusion in tracking robustness. To further improve the target tracking’s accuracy and reliability, we present a novel target tracking algorithm to use visual saliency and adaptive support vector machine (ASVM). Furthermore, the paper’s algorithm has been based on the mixture saliency of image features. These features include color, brightness, and sport feature. The execution process used visual saliency features and those common characteristics have been expressed as the target’s saliency. Numerous experiments demonstrate the effectiveness and timeliness of the proposed target tracking algorithm in video sequences where the target objects undergo large changes in pose, scale, and illumination.