TY - JOUR
T1 - Sequential Fish Catch Counter Using Vision-based Fish Detection and Tracking
AU - Tanaka, Riko
AU - Nakano, Teppei
AU - Ogawa, Tetsuji
N1 - Funding Information:
The authors would like to thank Seiya Ryozaki in Kochi Prefectural Fisheries Experimental Station for sharing the onboard video data.
Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - An attempt has been made to develop a system for sequentially counting the number of fish caught using images taken on board. Fish catch counting for each local sea area contributes to fishery resource management and decision support for efficient operation. In this case, visual information is helpful for an intuitive explanation. The developed system consists of fish detection, fish tracking, and overdetected track deletion: to count fish robustly to its movement around on a deck, the fish detection stage attempts to absorb changes in the appearance of the fish, while the tracking stage dares not to use the appearance information to prevent the tracks from being unduly disconnected. Experimental comparisons using onboard video data of bullet tuna trolling demonstrated that the system could count fish with 89% precision and 87% recall.
AB - An attempt has been made to develop a system for sequentially counting the number of fish caught using images taken on board. Fish catch counting for each local sea area contributes to fishery resource management and decision support for efficient operation. In this case, visual information is helpful for an intuitive explanation. The developed system consists of fish detection, fish tracking, and overdetected track deletion: to count fish robustly to its movement around on a deck, the fish detection stage attempts to absorb changes in the appearance of the fish, while the tracking stage dares not to use the appearance information to prevent the tracks from being unduly disconnected. Experimental comparisons using onboard video data of bullet tuna trolling demonstrated that the system could count fish with 89% precision and 87% recall.
KW - bullet tuna trolling
KW - Deep neural networks
KW - fish catch counting
KW - object detection
KW - object tracking
UR - http://www.scopus.com/inward/record.url?scp=85131685207&partnerID=8YFLogxK
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U2 - 10.1109/OCEANSChennai45887.2022.9775327
DO - 10.1109/OCEANSChennai45887.2022.9775327
M3 - Conference article
AN - SCOPUS:85131685207
SN - 0197-7385
JO - Oceans Conference Record (IEEE)
JF - Oceans Conference Record (IEEE)
T2 - OCEANS 2022 - Chennai
Y2 - 21 February 2022 through 24 February 2022
ER -
