Statistics Driven Suspicious Event Detection of Fishing Vessels Based on AIS Data

EasyChair Preprint 9723

Abstract

Fishing vessels have been widely used in contraband activities, and are also highly vulnerable to accidents, malfunction of the engines, etc. Fishing vessels are also reported in incidents by roaming in restricted border areas raising tensions across the nations. Timely monitoring and tracking of the fishing vessels will be needed such that it can improve the vigilance on the fishing vessel in contraband activities, provide rescue in case of accidents, malfunction of the vessel, or alarm in the case of sailing in restricted areas. In this paper, we propose an automated algorithm to detect any suspicious activity of the fishing vessel in real time, which can alarm the concerned authorities to take the necessary action. The algorithm is based on how frequent the fishing vessels transmit the automatic identification system (AIS) data. Monitoring the fishing vessels all the time is not necessary and is also likely infeasible. Knowing the limitation, we propose a statistics-driven threshold, based on which, we can reduce the instances for which we have to give attention to the fishing vessels.

Keyphrases: AIS data, Box plots, Fishing vessels, Vessel type, marine traffic monitoring, statistics

@booklet{EasyChair:9723,
  author    = {Harikumar Radhakrishnan and Saikat Bank and R Bharath and C P Ramanarayanan},
  title     = {Statistics Driven Suspicious Event Detection of Fishing Vessels Based on AIS Data},
  howpublished = {EasyChair Preprint 9723},
  year      = {EasyChair, 2023}}