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 |
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All Science Journal Classification (ASJC) codes
- Signal Processing
- Computer Vision and Pattern Recognition
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Learning structured visual dictionary for object tracking. / Yang, Fan; Lu, Huchuan; Yang, Ming Hsuan.
In: Image and Vision Computing, Vol. 31, No. 12, 01.01.2013, p. 992-999.Research output: Contribution to journal › Article
TY - JOUR
T1 - Learning structured visual dictionary for object tracking
AU - Yang, Fan
AU - Lu, Huchuan
AU - Yang, Ming Hsuan
PY - 2013/1/1
Y1 - 2013/1/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84888112703&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84888112703&partnerID=8YFLogxK
U2 - 10.1016/j.imavis.2013.09.008
DO - 10.1016/j.imavis.2013.09.008
M3 - Article
AN - SCOPUS:84888112703
VL - 31
SP - 992
EP - 999
JO - Image and Vision Computing
JF - Image and Vision Computing
SN - 0262-8856
IS - 12
ER -