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Asian Conference on Computer Vision

ACCV 2016: Computer Vision – ACCV 2016 pp 288–304Cite as

Model-Free Multiple Object Tracking with Shared Proposals

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10112))

Abstract

Most previous methods for tracking of multiple objects follow the conventional “tracking by detection” scheme and focus on improving the performance of category-specific object detectors as well as the between-frame tracklet association. These methods are therefore heavily sensitive to the performance of the object detectors, leading to limited application scenarios. In this work, we overcome this issue by a novel model-free framework that incorporates generic category-independent object proposals without the need to pretrain any object detectors. In each frame, our method generates a small number of target object proposals that are shared by multiple objects regardless of their category. This significantly improves the search efficiency in comparison to the traditional dense sampling approach. To further increase the discriminative power of our tracker among targets, we treat all other object proposals as the negative samples, i.e. as “distractors”, and update them in an online fashion. For a comprehensive evaluation, we test on the PETS benchmark datasets as well as a new MOOT benchmark dataset that contains more challenging videos. Results show that our method achieves superior performance in terms of both computational speed and tracking accuracy metrics.

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Acknowledgement

This work was supported under the Australian Research Council’s Discovery Projects funding scheme (project DP150104645, DP120103896), Linkage Projects funding scheme (LP100100588), ARC Centre of Excellence on Robotic Vision (CE140100016).

Author information

Authors and Affiliations

  1. Australian National University, Canberra, Australia

    Gao Zhu, Fatih Porikli & Hongdong Li

  2. Data61/CSIRO, Canberra, Australia

    Fatih Porikli

  3. ARC Centre of Excellence for Robotic Vision, Canberra, Australia

    Fatih Porikli & Hongdong Li

Authors
  1. Gao Zhu
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  2. Fatih Porikli
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  3. Hongdong Li
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Corresponding author

Correspondence to Gao Zhu .

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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Zhu, G., Porikli, F., Li, H. (2017). Model-Free Multiple Object Tracking with Shared Proposals. In: Lai, SH., Lepetit, V., Nishino, K., Sato, Y. (eds) Computer Vision – ACCV 2016. ACCV 2016. Lecture Notes in Computer Science(), vol 10112. Springer, Cham. https://doi.org/10.1007/978-3-319-54184-6_18

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  • DOI: https://doi.org/10.1007/978-3-319-54184-6_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-54183-9

  • Online ISBN: 978-3-319-54184-6

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