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SMAT: Smart Multiple Affinity Metrics for Multiple Object Tracking

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Image Analysis and Recognition (ICIAR 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12132))

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Abstract

This research introduces a novel multiple object tracking algorithm called SMAT (Smart Multiple Affinity Metric Tracking) that works as an online tracking-by-detection approach. The use of various characteristics from observation is established as a critical factor for improving tracking performance. By using the position, motion, appearance, and a correction component, our approach achieves an accuracy comparable to state of the art trackers. We use the optical flow to track the motion of the objects, we show that tracking accuracy can be improved by using a neural network to select key points to be tracked by the optical flow. The proposed algorithm is evaluated by using the KITTI Tracking Benchmark for the class CAR.

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Authors and Affiliations

  1. CSAI Laboratory at ENGIE, Paris, France

    Nicolas Franco Gonzalez, Andres Ospina & Philippe Calvez

Authors
  1. Nicolas Franco Gonzalez
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  2. Andres Ospina
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  3. Philippe Calvez
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Corresponding authors

Correspondence to Nicolas Franco Gonzalez , Andres Ospina or Philippe Calvez .

Editor information

Editors and Affiliations

  1. University of Porto, Porto, Portugal

    Aurélio Campilho

  2. University of Waterloo, Waterloo, ON, Canada

    Fakhri Karray

  3. University of Waterloo, Waterloo, ON, Canada

    Zhou Wang

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Gonzalez, N.F., Ospina, A., Calvez, P. (2020). SMAT: Smart Multiple Affinity Metrics for Multiple Object Tracking. In: Campilho, A., Karray, F., Wang, Z. (eds) Image Analysis and Recognition. ICIAR 2020. Lecture Notes in Computer Science(), vol 12132. Springer, Cham. https://doi.org/10.1007/978-3-030-50516-5_5

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  • DOI: https://doi.org/10.1007/978-3-030-50516-5_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-50515-8

  • Online ISBN: 978-3-030-50516-5

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