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Online Multi-Person Tracking Based on Metric Learning

  • Changyong Yu
  • Min Yang
  • Yanmei Dong
  • Mingtao PeiEmail author
  • Yunde Jia
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9916)

Abstract

The correct associations of detections and tracklets are the key to online multi-person tracking. Good appearance models can guide data association and play an important role in the association. In this paper, we construct a discriminative appearance model by using metric learning which can obtain accurate appearance affinities with human appearance variations. The novel appearance model can significantly guide data association. Furthermore, the model is learned incrementally according to the association results and its parameters are automatically updated to be suitable for the next online tracking. Based on an online tracking-by-detection framework, our method achieves reliable tracking of multiple persons even in complex scenes. Our experimental evaluation on publicly available data sets shows that the proposed online multi-person tracking method works well.

Keywords

Online tracking Metric learning Multi-person tracking Appearance model 

Notes

Acknowledgments

This work was supported in part by the Natural Science Foundation of China (NSFC) under Grant No. 61472038 and No. 61375044, and Beijing Key Laboratory of Advanced Information Science and Network Technology (No. XDXX1601).

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Changyong Yu
    • 1
  • Min Yang
    • 1
  • Yanmei Dong
    • 1
  • Mingtao Pei
    • 1
    Email author
  • Yunde Jia
    • 1
  1. 1.Beijing Laboratory of Intelligent Information Technology, School of Computer ScienceBeijing Institute of TechnologyBeijingPeople’s Republic of China

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