Journal
INTERNATIONAL JOURNAL OF COMPUTER VISION
Volume 118, Issue 3, Pages 337-363Publisher
SPRINGER
DOI: 10.1007/s11263-015-0879-9
Keywords
Visual tracking; Similarity measure; Fusion
Categories
Funding
- National Natural Science Foundation of China (NSFC) [61222308, 61572207, 61573160, 61173120]
- Program for New Century Excellent Talents in University [NCET-12-0217]
- NSF [OIA-1027897, IIS-1302164]
- China 973 Program [2012CB316300]
- National Natural Science Foundation of China (NSFC) [61222308, 61572207, 61573160, 61173120]
- Program for New Century Excellent Talents in University [NCET-12-0217]
- NSF [OIA-1027897, IIS-1302164]
- China 973 Program [2012CB316300]
- Div Of Information & Intelligent Systems
- Direct For Computer & Info Scie & Enginr [1302164, 1302700] Funding Source: National Science Foundation
Ask authors/readers for more resources
Multiple features' integration and context structure of unlabeled data have proven their effectiveness in enhancing similarity measures in many applications of computer vision. However, in similarity based object tracking, integration of multiple features has been rarely studied. In contrast to conventional tracking approaches that utilize pairwise similarity for template matching, our approach contributes in two different aspects. First, multiple features are integrated into a unified similarity to enhance the discriminative ability of similarity measurements. Second, the neighborhood context of the samples in forthcoming frame are employed to further improve the measurements. We utilize a diffusion process on a tensor product graph to achieve these goals. The obtained approach is validated on numerous challenging video sequences, and the experimental results demonstrate that it outperforms state-of-the-art t racking methods.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available