A4 Conference proceedings

Fish Detection from Low Visibility Underwater Videos


Publication Details
Authors: Shevchenko Violetta, Eerola Tuomas, Kaarna Arto
Publication year: 2018
Language: English
Related Journal or Series Information: International Conference on Pattern Recognition
Title of parent publication: 2018 24th International Conference on Pattern Recognition (ICPR)
Journal acronym: ICPR
Start page: 1971
End page: 1976
Number of pages: 6
eISBN: 978-1-5386-3788-3
ISSN: 1051-4651
JUFO-Level of this publication: 1
Open Access: Not an Open Access publication

Abstract

Counting and tracking fish populations is important
for conservation purposes as well as for the fishing industry.
Various non-invasive automatic fish counters exist based on such
principles as resistivity, light beams and sonar. However, such
methods typically cannot make distinction between fish and other
passing objects, and moreover, cannot recognize different species.
Computer vision techniques provide an attractive alternative
for building a more robust and versatile fish counting systems.
In this paper we present the fish detection framework for
noisy videos captured in water with low visibility. For this
purpose, we compare three background subtraction methods
for the task. Moreover, we propose necessary post-processing
steps and heuristics to detect the fish and separate them from
other moving objects. The results showed that by choosing an
appropriate background subtraction method, it is possible to
achieve a satisfying detection accuracy of 80% and 60% for
two challenging datasets. The proposed method will form a basis
for the future development of fish species identification methods.


Last updated on 2019-13-03 at 12:00