Abstract
In this paper, we propose a visual tracking algorithm by incorporating the appearance information gathered from two collaborative feature sets and exploiting its geometric structures. A structured visual dictionary (SVD) can be learned from both appearance and geometric structure, thereby enhancing its discriminative strength between the foreground object and the background. Experimental results show that the proposed tracking algorithm using SVD (SVDTrack) performs favorably against the state-of-the-art methods.
| Original language | English |
|---|---|
| Pages (from-to) | 992-999 |
| Number of pages | 8 |
| Journal | Image and Vision Computing |
| Volume | 31 |
| Issue number | 12 |
| DOIs | |
| Publication status | Published - 2013 Jan 1 |
All Science Journal Classification (ASJC) codes
- Signal Processing
- Computer Vision and Pattern Recognition
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Yang, F., Lu, H., & Yang, M. H. (2013). Learning structured visual dictionary for object tracking. Image and Vision Computing, 31(12), 992-999. https://doi.org/10.1016/j.imavis.2013.09.008