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Learning Local Feature Descriptors for Multiple Object Tracking

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Computer Vision – ACCV 2020 (ACCV 2020)

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Abstract

The present study aims at learning class-agnostic embedding, which is suitable for Multiple Object Tracking (MOT). We demonstrate that the learning of local feature descriptors could provide a sufficient level of generalization. Proposed embedding function exhibits on-par performance with its dedicated person re-identification counterparts in their target domain and outperforms them in others. Through its utilization, our solutions achieve state-of-the-art performance in a number of MOT benchmarks, which includes CVPR’19 Tracking Challenge.

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Notes

  1. 1.

    Corresponding VLFeat [46] implementations were employed similarly to Ref. [26].

  2. 2.

    See the processing time comparison in the supplementary material.

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Authors and Affiliations

  1. Samsung R&D Institute Ukraine (SRK), 57 L’va Tolstogo Street, Kyiv, 01032, Ukraine

    Dmytro Mykheievskyi, Dmytro Borysenko & Viktor Porokhonskyy

Authors
  1. Dmytro Mykheievskyi
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  2. Dmytro Borysenko
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  3. Viktor Porokhonskyy
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Corresponding author

Correspondence to Viktor Porokhonskyy .

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Editors and Affiliations

  1. Waseda University, Tokyo, Japan

    Hiroshi Ishikawa

  2. Institute of Automation of Chinese Academy of Sciences, Beijing, China

    Cheng-Lin Liu

  3. Czech Technical University in Prague, Prague, Czech Republic

    Tomas Pajdla

  4. University of Pennsylvania, Philadelphia, PA, USA

    Jianbo Shi

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Mykheievskyi, D., Borysenko, D., Porokhonskyy, V. (2021). Learning Local Feature Descriptors for Multiple Object Tracking. In: Ishikawa, H., Liu, CL., Pajdla, T., Shi, J. (eds) Computer Vision – ACCV 2020. ACCV 2020. Lecture Notes in Computer Science(), vol 12623. Springer, Cham. https://doi.org/10.1007/978-3-030-69532-3_34

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