Open AccessThree-Dimensional Stereo Vision Tracking of Multiple Free-Swimming Fish for Low Frame Rate Video
Author(s) -
Maria Gemel B. Palconit,
Ronnie Concepcion,
Jonnel Alejandrino,
Michael Pareja,
Vincent Jan D. Almero,
Argel A. Bandala,
Ryan Rhay P. Vicerra,
Edwin Sybingco,
Elmer P. Dadios,
R.N.G. Naguib
Publication year - 2021
Publication title -
journal of advanced computational intelligence and intelligent informatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.172
H-Index - 20
eISSN - 1343-0130
pISSN - 1883-8014
DOI - 10.20965/jaciii.2021.p0639
Subject(s) - computer science , artificial intelligence , frame (networking) , computer vision , symbolic regression , frame rate , kriging , regression , linear regression , context (archaeology) , pattern recognition (psychology) , machine learning , statistics , mathematics , genetic programming , telecommunications , paleontology , biology
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.