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Segmentation methods for visual tracking of deep-ocean jellyfish using a conventional camera

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2 Author(s)
Rife, J. ; Stanford Univ., CA, USA ; Rock, S.M.

This paper presents a vision algorithm that enables automated jellyfish tracking using remotely operated vehicles (ROVs) or autonomous underwater vehicles (AUVs). The discussion focuses on algorithm design. The introduction provides a novel performance-assessment tool, called segmentation efficiency, which aids in matching potential vision algorithms to the jelly-tracking task. This general-purpose tool evaluates the inherent applicability of various algorithms to particular tracking applications. This tool is applied to the problem of tracking transparent jellyfish under uneven time-varying illumination in particle-filled scenes. The result is the selection of a fixed-gradient threshold-based vision algorithm. This approach, implemented as part of a pilot aid for the Monterey Bay Aquarium Research Institute's ROV Ventana, has demonstrated automated jelly tracking for as long as 89 min.

Published in:

Oceanic Engineering, IEEE Journal of  (Volume:28 ,  Issue: 4 )