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Object Tracking via Partial Least Squares Analysis

Title
Object Tracking via Partial Least Squares Analysis
Author
Ming-hsuan Yang
Keywords
Appearance model; object tracking; partial least squares analysis
Issue Date
2012-06
Publisher
INSTITUTE OF ELECTRICAL AND ELECTRONICS ENGINEERS
Citation
IEEE transactions on image processing,Vol.21 No.10 [2012],p4454-4465
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.
URI
http://ieeexplore.ieee.org/document/6224182/?reload=truehttp://hdl.handle.net/20.500.11754/40155
ISSN
1057-7149
DOI
10.1109/TIP.2012.2205700
Appears in Collections:
COLLEGE OF ENGINEERING[S](공과대학) > COMPUTER SCIENCE AND ENGINEERING(컴퓨터공학부) > Articles
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