Zebrafish tracking using YOLOv2 and Kalman filter
Open Access
- 5 February 2021
- journal article
- research article
- Published by Springer Science and Business Media LLC in Scientific Reports
- Vol. 11 (1), 1-14
- https://doi.org/10.1038/s41598-021-81997-9
Abstract
Fish show rapid movements in various behavioral activities or associated with the presence of food. However, in periods of rapid movement, the rate at which occlusion occurs among the fish is quite high, causing inconsistency in the detection and tracking of fish, hindering the fish's identity and behavioral trajectory over a long period of time. Although some algorithms have been proposed to solve these problems, most of their applications were made in groups of fish that swim in shallow water and calm behavior, with few sudden movements. To solve these problems, a convolutional network of object recognition, YOLOv2, was used to delimit the region of the fish heads to optimize individual fish detection. In the tracking phase, the Kalman filter was used to estimate the best state of the fish's head position in each frame and, subsequently, the trajectories of each fish were connected among the frames. The results of the algorithm show adequate performances in the trajectories of groups of zebrafish that exhibited rapid movements.Keywords
Funding Information
- Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
- Fundação de Amparo à Pesquisa e ao Desenvolvimento Científico e Tecnológico do Maranhão
This publication has 30 references indexed in Scilit:
- Both information and social cohesion determine collective decisions in animal groupsProceedings of the National Academy of Sciences of the United States of America, 2013
- ZebraZoom: an automated program for high-throughput behavioral analysis and categorizationFrontiers in Neural Circuits, 2013
- Video multitracking of fish behaviour: a synthesis and future perspectivesFish and Fisheries, 2012
- Behavioral Measure of Frequency Detection and Discrimination in the Zebrafish, Danio rerioZebrafish, 2012
- Video tracking in the extreme: A new possibility for tracking nocturnal underwater transparent animals with fluorescent elastomer tagsBehavior Research Methods, 2011
- Associative learning in zebrafish (Danio rerio) in the plus mazeBehavioural Brain Research, 2010
- A comparative study of Artificial Bee Colony algorithmApplied Mathematics and Computation, 2009
- Automated visual tracking for studying the ontogeny of zebrafish swimmingJournal of Experimental Biology, 2008
- Quantification of shoaling behaviour in zebrafish (Danio rerio)Behavioural Brain Research, 2007
- The EthoVision video tracking system—A tool for behavioral phenotyping of transgenic micePhysiology & Behavior, 2001