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Automatically Detect and Track Multiple Fish Swimming in Shallow Water with Frequent Occlusion

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  • Zhi-Ming Qian
  • Xi En Cheng
  • Yan Qiu Chen

Abstract

Due to its universality, swarm behavior in nature attracts much attention of scientists from many fields. Fish schools are examples of biological communities that demonstrate swarm behavior. The detection and tracking of fish in a school are of important significance for the quantitative research on swarm behavior. However, different from other biological communities, there are three problems in the detection and tracking of fish school, that is, variable appearances, complex motion and frequent occlusion. To solve these problems, we propose an effective method of fish detection and tracking. In this method, first, the fish head region is positioned through extremum detection and ellipse fitting; second, The Kalman filtering and feature matching are used to track the target in complex motion; finally, according to the feature information obtained by the detection and tracking, the tracking problems caused by frequent occlusion are processed through trajectory linking. We apply this method to track swimming fish school of different densities. The experimental results show that the proposed method is both accurate and reliable.

Suggested Citation

  • Zhi-Ming Qian & Xi En Cheng & Yan Qiu Chen, 2014. "Automatically Detect and Track Multiple Fish Swimming in Shallow Water with Frequent Occlusion," PLOS ONE, Public Library of Science, vol. 9(9), pages 1-12, September.
    • Handle: RePEc:plo:pone00:0106506
    DOI: 10.1371/journal.pone.0106506
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    1. Aharon Weissbrod & Alexander Shapiro & Genadiy Vasserman & Liat Edry & Molly Dayan & Assif Yitzhaky & Libi Hertzberg & Ofer Feinerman & Tali Kimchi, 2013. "Automated long-term tracking and social behavioural phenotyping of animal colonies within a semi-natural environment," Nature Communications, Nature, vol. 4(1), pages 1-10, October.
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