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Underwater Fish Tracking-by-Detection: An Adaptive Tracking Approach

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Pattern Recognition, Computer Vision, and Image Processing. ICPR 2022 International Workshops and Challenges (ICPR 2022)

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Abstract

High distortion and complex marine environment pose severe challenges to underwater tracking. In this paper, we propose a simple, template-free Adaptive Euclidean Tracking (AET) approach for underwater fish tracking by regarding tracking as a specific case of instance detection. The proposed method exploits the advanced detection framework to track the fish in underwater imagery without any image enhancement techniques. The proposed method achieves comparable performance on the DeepFish dataset, with 22% and 14% improvement in precision and success over state-of-art trackers.

D. Velayudhan and A. Ghimire—Authors make equal contributions to this work.

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Acknowledgments

This publication acknowledges the support provided by the Khalifa University of Science and Technology under Faculty Start Up grants FSU-2022-003 Award No. 84740 0 0401.

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Authors and Affiliations

  1. Khalifa University of Science and Technology, Abu Dhabi, UAE

    Divya Velayudhan, Adarsh Ghimire, Jorge Dias, Naoufel Werghi & Sajid Javed

Authors
  1. Divya Velayudhan
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  2. Adarsh Ghimire
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  3. Jorge Dias
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  4. Naoufel Werghi
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  5. Sajid Javed
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Corresponding author

Correspondence to Divya Velayudhan .

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Editors and Affiliations

  1. York University, Toronto, ON, Canada

    Jean-Jacques Rousseau

  2. University of Ontario Institute of Technology, Oshawa, ON, Canada

    Bill Kapralos

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Velayudhan, D., Ghimire, A., Dias, J., Werghi, N., Javed, S. (2023). Underwater Fish Tracking-by-Detection: An Adaptive Tracking Approach. In: Rousseau, JJ., Kapralos, B. (eds) Pattern Recognition, Computer Vision, and Image Processing. ICPR 2022 International Workshops and Challenges. ICPR 2022. Lecture Notes in Computer Science, vol 13645. Springer, Cham. https://doi.org/10.1007/978-3-031-37731-0_37

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  • DOI: https://doi.org/10.1007/978-3-031-37731-0_37

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  • Online ISBN: 978-3-031-37731-0

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