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Data Association Based Multi-target Tracking Using a Joint Formulation

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Computer Vision – ACCV 2016 (ACCV 2016)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10114))

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Abstract

We revisit the classical conditional random filed based tracking-by-detection framework for multi-target tracking, in which function factors associating pairs of short tracklets in a long term are modeled to produce final tracks. Unlike most previous approaches which only focus on modeling feature difference for distinguishing pairs of targets, we propose to directly model the joint formulation of pairs of tracklets for association in the CRF framework. To this end, we use a Hough Forest (HF) based learning framework to effectively learn a discriminative codebook of features among tracklets by utilizing appearance and motion cues stored in the leaf nodes. Given the learned codebook, the joint formulation of tracklet pairs can be directly modeled in a nonparametric manner by defining a sharing and excluding matrix. Then all of the statistics required in CRF inference can be directly estimated. Extensive experiments have been conducted on several public datasets, and the performance is comparable to the state of the art.

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Acknowledgement

This work was supported by the National Natural Science Foundation of China under Grant 61271328, 61671484 and 61401170.

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Authors and Affiliations

  1. National Key Laboratory of Science and Technology on Multispectral Information Processing, School of Automation, Huazhong University of Science and Technology, Wuhan, China

    Jun Xiang, Changxin Gao & Nong Sang

  2. Hubei Key Laboratory of Intelligent Wireless Communications, South-Central University for Nationalities, Wuhan, China

    Jun Xiang & Jianhua Hou

Authors
  1. Jun Xiang
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  2. Jianhua Hou
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  3. Changxin Gao
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  4. Nong Sang
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Corresponding author

Correspondence to Nong Sang .

Editor information

Editors and Affiliations

  1. National Tsing Hua University, Hsinchu, Taiwan

    Shang-Hong Lai

  2. Graz University of Technology, Graz, Austria

    Vincent Lepetit

  3. Drexel University, Philadelphia, Pennsylvania, USA

    Ko Nishino

  4. The University of Tokyo, Tokyo, Japan

    Yoichi Sato

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Xiang, J., Hou, J., Gao, C., Sang, N. (2017). Data Association Based Multi-target Tracking Using a Joint Formulation. In: Lai, SH., Lepetit, V., Nishino, K., Sato, Y. (eds) Computer Vision – ACCV 2016. ACCV 2016. Lecture Notes in Computer Science(), vol 10114. Springer, Cham. https://doi.org/10.1007/978-3-319-54190-7_15

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  • DOI: https://doi.org/10.1007/978-3-319-54190-7_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-54189-1

  • Online ISBN: 978-3-319-54190-7

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