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On-road vehicle tracking using keypoint-based representation and online co-training

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Abstract

This paper addresses the issue of tracking an on-road vehicle from images taken by a camera inside a moving platform. Although existing methods have achieved successful results on tracking general objects such as face and pedestrian, when facing complicated road environments, their performance is unsatisfactory. Therefore, a method specialized for on-road vehicle tracking is needed. The proposed tracking method follows “tracking-by-detection” framework and uses co-training to construct a system with increasing learning ability. The method is mainly composed of tracker, detector, and error collector. The tracker uses keypoint matching to estimate the new location from frame to frame. The detector then fine tunes the location by using templates and knowledge-based methods and outputs the bounding box of the vehicle. At last, the error collector catches all possible errors from tracker and detector, and adds them into a dictionary to avoid similar errors in the future. Due to the error collector, the tracker and the detector can reinforce each other during tracking, thus, we also refer to the detector as online detector. This co-training mechanism leads to an efficient offline detector, which employs integrated information, including classified keypoints, templates, and symmetry, to perform “reappearance detection” when object disappears. The proposed method has been successfully validated by performing experiments with an onboard camera mounted on an on-road vehicle.

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Acknowledgments

This work was supported by the National Nature Science Foundation of China (Grant No. 61071162).

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Authors and Affiliations

  1. College of Computer Science, Sichuan University, Chengdu, 610065, China

    Shuo Yang, Jie Xu, Yong Chen & Minghui Wang

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  1. Shuo Yang
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  2. Jie Xu
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  3. Yong Chen
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  4. Minghui Wang
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Correspondence to Shuo Yang.

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Yang, S., Xu, J., Chen, Y. et al. On-road vehicle tracking using keypoint-based representation and online co-training. Multimed Tools Appl 72, 1561–1583 (2014). https://doi.org/10.1007/s11042-013-1453-5

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