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An Integrated Learning Framework for Pedestrian Tracking

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Part of the Lecture Notes in Computer Science book series (LNAI,volume 10363)

Abstract

Pedestrian tracking has been arguably addressed as a special topic beyond general object tracking. Although many learning or data driven object trackers as well as recent deep learning object trackers have shown excellent performance for general object tracking, they have limited success on pedestrian tracking because there exist three major challenges emerging from pedestrian tracking such as vast variations of human bodies, distraction from similar persons and complete occlusion. In this paper, we propose an integrated learning framework for pedestrian tracking to overcome these problems. It is demonstrated by the experimental results on the SVD-B dataset that our proposed framework can achieve competitive results in comparison with state-of-the-art object trackers under the evaluation of the precision and success rate as well as fps.

Keywords

  • Pedestrian tracking
  • Deep re-id feature
  • Online learning
  • R-CNN
  • Switching detection
  • Precision and success plots

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  • DOI: 10.1007/978-3-319-63315-2_9
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Notes

  1. 1.

    The source code and dataset are available at http://github.com/prinsphield/ILFPT.

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Acknowledgments

This work was supported by the Natural Science Foundation of China for Grant 61171138.

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Correspondence to Jinwen Ma .

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Xiao, T., Ma, J. (2017). An Integrated Learning Framework for Pedestrian Tracking. In: Huang, DS., Hussain, A., Han, K., Gromiha, M. (eds) Intelligent Computing Methodologies. ICIC 2017. Lecture Notes in Computer Science(), vol 10363. Springer, Cham. https://doi.org/10.1007/978-3-319-63315-2_9

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  • DOI: https://doi.org/10.1007/978-3-319-63315-2_9

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