23 February 2016 Effective real-time vehicle tracking using discriminative sparse coding on local patches
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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.
© 2016 SPIE and IS&T
XiangJun Chen, XiangJun Chen, Feiyue Ye, Feiyue Ye, Yaduan Ruan, Yaduan Ruan, Qimei Chen, Qimei Chen, } "Effective real-time vehicle tracking using discriminative sparse coding on local patches," Journal of Electronic Imaging 25(1), 013035 (23 February 2016). https://doi.org/10.1117/1.JEI.25.1.013035 . Submission:

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