Adaptive and discriminative metric differential tracking

Nan Jiang; Wenyu Liu; Ying Wu

doi:10.1109/CVPR.2011.5995716

Adaptive and discriminative metric differential tracking

Nan Jiang^*, Wenyu Liu, Ying Wu

^*Corresponding author for this work

Electrical and Computer Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

37 Scopus citations

Abstract

Matching the visual appearances of the target over consecutive image frames is the most critical issue in video-based object tracking. Choosing an appropriate distance metric for matching determines its accuracy and robustness, and significantly influences the tracking performance. This paper presents a new tracking approach that incorporates adaptive metric into differential tracking method. This new approach automatically learns an optimal distance metric for more accurate matching, and obtains a closed-form analytical solution to motion estimation and differential tracking. Extensive experiments validate the effectiveness of adaptive metric, and demonstrate the improved performance of the proposed new tracking method.

Original language	English (US)
Title of host publication	2011 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2011
Pages	1161-1168
Number of pages	8
DOIs	https://doi.org/10.1109/CVPR.2011.5995716
State	Published - Sep 22 2011
Event	2011 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2011 - Colorado Springs, CO, United States Duration: Jun 20 2011 → Jun 25 2011

Other

Other	2011 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2011
Country/Territory	United States
City	Colorado Springs, CO
Period	6/20/11 → 6/25/11

ASJC Scopus subject areas

Software
Computer Vision and Pattern Recognition

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10.1109/CVPR.2011.5995716

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title = "Adaptive and discriminative metric differential tracking",

abstract = "Matching the visual appearances of the target over consecutive image frames is the most critical issue in video-based object tracking. Choosing an appropriate distance metric for matching determines its accuracy and robustness, and significantly influences the tracking performance. This paper presents a new tracking approach that incorporates adaptive metric into differential tracking method. This new approach automatically learns an optimal distance metric for more accurate matching, and obtains a closed-form analytical solution to motion estimation and differential tracking. Extensive experiments validate the effectiveness of adaptive metric, and demonstrate the improved performance of the proposed new tracking method.",

author = "Nan Jiang and Wenyu Liu and Ying Wu",

year = "2011",

month = sep,

day = "22",

doi = "10.1109/CVPR.2011.5995716",

language = "English (US)",

isbn = "9781457703942",

pages = "1161--1168",

booktitle = "2011 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2011",

note = "2011 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2011 ; Conference date: 20-06-2011 Through 25-06-2011",

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T1 - Adaptive and discriminative metric differential tracking

AU - Jiang, Nan

AU - Liu, Wenyu

AU - Wu, Ying

PY - 2011/9/22

Y1 - 2011/9/22

N2 - Matching the visual appearances of the target over consecutive image frames is the most critical issue in video-based object tracking. Choosing an appropriate distance metric for matching determines its accuracy and robustness, and significantly influences the tracking performance. This paper presents a new tracking approach that incorporates adaptive metric into differential tracking method. This new approach automatically learns an optimal distance metric for more accurate matching, and obtains a closed-form analytical solution to motion estimation and differential tracking. Extensive experiments validate the effectiveness of adaptive metric, and demonstrate the improved performance of the proposed new tracking method.

AB - Matching the visual appearances of the target over consecutive image frames is the most critical issue in video-based object tracking. Choosing an appropriate distance metric for matching determines its accuracy and robustness, and significantly influences the tracking performance. This paper presents a new tracking approach that incorporates adaptive metric into differential tracking method. This new approach automatically learns an optimal distance metric for more accurate matching, and obtains a closed-form analytical solution to motion estimation and differential tracking. Extensive experiments validate the effectiveness of adaptive metric, and demonstrate the improved performance of the proposed new tracking method.

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M3 - Conference contribution

AN - SCOPUS:80052884532

SN - 9781457703942

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EP - 1168

BT - 2011 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2011

T2 - 2011 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2011

Y2 - 20 June 2011 through 25 June 2011

ER -

Adaptive and discriminative metric differential tracking

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