Fishing Behavior Detection and Analysis of Squid Fishing Vessel Based on Multiscale Trajectory Characteristics

Fan Zhang, Baoxin Yuan, L. Huang, Yuanqiao Wen, Xue Yang, R. Song, P.H.A.J.M. van Gelder

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Accurate fishing activity detection from the trajectories of fishing vessels can not only achieve high-precision fishery management but also ensure the reasonable and sustainable development of marine fishery resources. This paper proposes a new method to detect fishing vessels’ fishing activities based on the defined local dynamic parameters and global statistical characteristics of vessel trajectories. On a local scale, the stop points and points of interest (POIs) in the vessel trajectory are extracted. Voyage extraction can then be conducted on this basis. After that, multiple characteristics based on motion and morphology on a global scale are defined to construct a logistic regression model for fishing behavior detection. To verify the effectiveness and feasibility of the method, vessel trajectory data, and fishing log data collected from Chinese ocean squid fishing vessels in Argentine waters in 2020 are integrated for fishing operation detection. Multiple evaluation metrics show that the proposed method can provide robust and accurate recognition results. Moreover, further analysis of the temporal and spatial distribution and seasonal changes in squid fishing activities in Argentine waters has been performed. A more refined assessment of the fishing activities of individual fishing vessels can also be provided quantitatively. All the results above can benefit the regulation of fishing activities.
Original languageEnglish
Article number1245
JournalJournal of Marine Science and Engineering
Issue number6
Publication statusPublished - 2023


  • fishing behavior
  • fishery management
  • statistical features of trajectory sequences
  • logistic regression
  • sliding window


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