Abstract
We propose an object tracking algorithm that learns a set of appearance models for adaptive discriminative object representation. In this paper, object tracking is posed as a binary classification problem in which the correlation of object appearance and class labels from foreground and background is modeled by partial least squares (PLS) analysis, for generating a low-dimensional discriminative feature subspace. As object appearance is temporally correlated and likely to repeat over time, we learn and adapt multiple appearance models with PLS analysis for robust tracking. The proposed algorithm exploits both the ground truth appearance information of the target labeled in the first frame and the image observations obtained online, thereby alleviating the tracking drift problem caused by model update. Experiments on numerous challenging sequences and comparisons to state-of-the-art methods demonstrate favorable performance of the proposed tracking algorithm.
| Original language | English |
|---|---|
| Article number | 6224182 |
| Pages (from-to) | 4454-4465 |
| Number of pages | 12 |
| Journal | IEEE Transactions on Image Processing |
| Volume | 21 |
| Issue number | 10 |
| DOIs | |
| Publication status | Published - 2012 Sep 28 |
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All Science Journal Classification (ASJC) codes
- Software
- Computer Graphics and Computer-Aided Design
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Object tracking via partial least squares analysis. / Wang, Qing; Chen, Feng; Xu, Wenli; Yang, Ming Hsuan.
In: IEEE Transactions on Image Processing, Vol. 21, No. 10, 6224182, 28.09.2012, p. 4454-4465.Research output: Contribution to journal › Article
TY - JOUR
T1 - Object tracking via partial least squares analysis
AU - Wang, Qing
AU - Chen, Feng
AU - Xu, Wenli
AU - Yang, Ming Hsuan
PY - 2012/9/28
Y1 - 2012/9/28
N2 - We propose an object tracking algorithm that learns a set of appearance models for adaptive discriminative object representation. In this paper, object tracking is posed as a binary classification problem in which the correlation of object appearance and class labels from foreground and background is modeled by partial least squares (PLS) analysis, for generating a low-dimensional discriminative feature subspace. As object appearance is temporally correlated and likely to repeat over time, we learn and adapt multiple appearance models with PLS analysis for robust tracking. The proposed algorithm exploits both the ground truth appearance information of the target labeled in the first frame and the image observations obtained online, thereby alleviating the tracking drift problem caused by model update. Experiments on numerous challenging sequences and comparisons to state-of-the-art methods demonstrate favorable performance of the proposed tracking algorithm.
AB - We propose an object tracking algorithm that learns a set of appearance models for adaptive discriminative object representation. In this paper, object tracking is posed as a binary classification problem in which the correlation of object appearance and class labels from foreground and background is modeled by partial least squares (PLS) analysis, for generating a low-dimensional discriminative feature subspace. As object appearance is temporally correlated and likely to repeat over time, we learn and adapt multiple appearance models with PLS analysis for robust tracking. The proposed algorithm exploits both the ground truth appearance information of the target labeled in the first frame and the image observations obtained online, thereby alleviating the tracking drift problem caused by model update. Experiments on numerous challenging sequences and comparisons to state-of-the-art methods demonstrate favorable performance of the proposed tracking algorithm.
UR - http://www.scopus.com/inward/record.url?scp=84866604392&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84866604392&partnerID=8YFLogxK
U2 - 10.1109/TIP.2012.2205700
DO - 10.1109/TIP.2012.2205700
M3 - Article
AN - SCOPUS:84866604392
VL - 21
SP - 4454
EP - 4465
JO - IEEE Transactions on Image Processing
JF - IEEE Transactions on Image Processing
SN - 1057-7149
IS - 10
M1 - 6224182
ER -