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Journal of Electronic Imaging

Effective real-time vehicle tracking using discriminative sparse coding on local patches
Author(s): XiangJun Chen; Feiyue Ye; Yaduan Ruan; Qimei Chen
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Paper Abstract

A visual tracking framework that provides an object detector and tracker, which focuses on effective and efficient visual tracking in surveillance of real-world intelligent transport system applications, is proposed. The framework casts the tracking task as problems of object detection, feature representation, and classification, which is different from appearance model-matching approaches. Through a feature representation of discriminative sparse coding on local patches called DSCLP, which trains a dictionary on local clustered patches sampled from both positive and negative datasets, the discriminative power and robustness has been improved remarkably, which makes our method more robust to a complex realistic setting with all kinds of degraded image quality. Moreover, by catching objects through one-time background subtraction, along with offline dictionary training, computation time is dramatically reduced, which enables our framework to achieve real-time tracking performance even in a high-definition sequence with heavy traffic. Experiment results show that our work outperforms some state-of-the-art methods in terms of speed, accuracy, and robustness and exhibits increased robustness in a complex real-world scenario with degraded image quality caused by vehicle occlusion, image blur of rain or fog, and change in viewpoint or scale.

Paper Details

Date Published: 23 February 2016
PDF: 12 pages
J. Electron. Imaging. 25(1) 013035 doi: 10.1117/1.JEI.25.1.013035
Published in: Journal of Electronic Imaging Volume 25, Issue 1
Show Author Affiliations
XiangJun Chen, Nanjing Univ. (China)
Jiangsu Univ. of Technology (China)
Feiyue Ye, Jiangsu Univ. of Technology (China)
Yaduan Ruan, Nanjing Univ. (China)
Qimei Chen, Nanjing Univ. (China)

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