Three-Dimensional Stereo Vision Tracking of Multiple Free-Swimming Fish for Low Frame Rate Video

Three-dimensional multiple fish tracking has gained significant research interest in quantifying fish behavior. However, most tracking techniques use a high frame rate, which is currently not viable for real-time tracking applications. This study discusses multiple fish-tracking techniques using low...

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Vydáno v:Journal of advanced computational intelligence and intelligent informatics Ročník 25; číslo 5; s. 639 - 646
Hlavní autoři: Palconit, Maria Gemel B., II, Ronnie S. Concepcion, Alejandrino, Jonnel D., Pareja, Michael E., Almero, Vincent Jan D., Bandala, Argel A., Vicerra, Ryan Rhay P., Sybingco, Edwin, Dadios, Elmer P., Naguib, Raouf N. G.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Tokyo Fuji Technology Press Co. Ltd 01.09.2021
Témata:
Adaptive systems
Algorithms
Artificial neural networks
Centroids
Computing time
Fish
Frames per second
Fuzzy logic
Gaussian process
Informatics
Intelligence
Iterative methods
Kalman filters
Machine learning
Nonlinear systems
Regression
Regression analysis
Swimming
Tracking
Webcams
ISSN:1343-0130, 1883-8014
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Shrnutí:Three-dimensional multiple fish tracking has gained significant research interest in quantifying fish behavior. However, most tracking techniques use a high frame rate, which is currently not viable for real-time tracking applications. This study discusses multiple fish-tracking techniques using low-frame-rate sampling of stereo video clips. The fish were tagged and tracked based on the absolute error of the predicted indices using past and present fish centroid locations and a deterministic frame index. In the predictor sub-system, linear regression and machine learning algorithms intended for nonlinear systems, such as the adaptive neuro-fuzzy inference system (ANFIS), symbolic regression, and Gaussian process regression (GPR), were investigated. The results showed that, in the context of tagging and tracking accuracy, the symbolic regression attained the best performance, followed by the GPR, that is, 74% to 100% and 81% to 91%, respectively. Considering the computation time, symbolic regression resulted in the highest computing lag of approximately 946 ms per iteration, whereas GPR achieved the lowest computing time of 39 ms.
Bibliografie:ObjectType-Article-1
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ISSN:1343-0130
1883-8014
DOI:10.20965/jaciii.2021.p0639