Object tracking via partial least squares analysis

Qing Wang; Feng Chen; Wenli Xu; Ming Hsuan Yang

doi:10.1109/TIP.2012.2205700

Object tracking via partial least squares analysis

Qing Wang, Feng Chen, Wenli Xu, Ming Hsuan Yang

Department of Computer Science

Research output: Contribution to journal › Article › peer-review

118 Citations (Scopus)

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	https://doi.org/10.1109/TIP.2012.2205700
Publication status	Published - 2012

Bibliographical note

Funding Information:
Manuscript received October 3, 2011; revised March 17, 2012; accepted May 11, 2012. Date of publication June 22, 2012; date of current version September 13, 2012. The work of Q. Wang and F. Chen was supported in part by the Natural Science Foundation of China under Grant 61071131 and the Beijing Natural Science Foundation (NSF) under Grant 4122040. The work of W. Xu was supported in part by the National Key Basic Research and Development Program of China under Grant 2009CB320602. The work of M.-H. Yang was supported in part by the NSF CAREER under Grant 1149783 and NSF IIS under Grant 1152576. The associate editor coordinating the review of this manuscript and approving it for publication was Prof. Theo Gevers.

All Science Journal Classification (ASJC) codes

Software
Computer Graphics and Computer-Aided Design

Access to Document

10.1109/TIP.2012.2205700

Cite this

@article{7a669494b6e24d8a89f9f1b456719102,

title = "Object tracking via partial least squares analysis",

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.",

author = "Qing Wang and Feng Chen and Wenli Xu and Yang, {Ming Hsuan}",

note = "Funding Information: Manuscript received October 3, 2011; revised March 17, 2012; accepted May 11, 2012. Date of publication June 22, 2012; date of current version September 13, 2012. The work of Q. Wang and F. Chen was supported in part by the Natural Science Foundation of China under Grant 61071131 and the Beijing Natural Science Foundation (NSF) under Grant 4122040. The work of W. Xu was supported in part by the National Key Basic Research and Development Program of China under Grant 2009CB320602. The work of M.-H. Yang was supported in part by the NSF CAREER under Grant 1149783 and NSF IIS under Grant 1152576. The associate editor coordinating the review of this manuscript and approving it for publication was Prof. Theo Gevers.",

year = "2012",

doi = "10.1109/TIP.2012.2205700",

language = "English",

volume = "21",

pages = "4454--4465",

journal = "IEEE Transactions on Image Processing",

issn = "1057-7149",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

number = "10",

}

TY - JOUR

T1 - Object tracking via partial least squares analysis

AU - Wang, Qing

AU - Chen, Feng

AU - Xu, Wenli

AU - Yang, Ming Hsuan

N1 - Funding Information: Manuscript received October 3, 2011; revised March 17, 2012; accepted May 11, 2012. Date of publication June 22, 2012; date of current version September 13, 2012. The work of Q. Wang and F. Chen was supported in part by the Natural Science Foundation of China under Grant 61071131 and the Beijing Natural Science Foundation (NSF) under Grant 4122040. The work of W. Xu was supported in part by the National Key Basic Research and Development Program of China under Grant 2009CB320602. The work of M.-H. Yang was supported in part by the NSF CAREER under Grant 1149783 and NSF IIS under Grant 1152576. The associate editor coordinating the review of this manuscript and approving it for publication was Prof. Theo Gevers.

PY - 2012

Y1 - 2012

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 -

Object tracking via partial least squares analysis

Abstract

Bibliographical note

All Science Journal Classification (ASJC) codes

Access to Document

Other files and links

Fingerprint

Cite this