Please use this identifier to cite or link to this item: https://repositorio.ufrn.br/handle/123456789/31459
Title: Zebrafish tracking using YOLOv2 and Kalman filter
Authors: Barreiros, Marta de Oliveira
Dantas, Diego de Oliveira
Silva, Luis Claudio de Oliveira
Ribeiro, Sidarta Tollendal Gomes
Barros Filho, Allan Kardec Duailibe
Keywords: Zebrafish;YOLOv2 network;Kalman filter;Behavior, animal
Issue Date: 5-Feb-2021
Publisher: Springer Science and Business Media LLC.
Citation: BARREIROS, Marta de Oliveira; DANTAS, Diego de Oliveira; SILVA, Luís Claudio de Oliveira; RIBEIRO, Sidarta; BARROS, Allan Kardec. Zebrafish tracking using YOLOv2 and Kalman filter. Scientific Reports, [S.l.], v. 11, n. 1, p. 3219, fev. 2021. doi: http://dx.doi.org/10.1038/s41598-021-81997-9. Disponível em: https://www.nature.com/articles/s41598-021-81997-9. Acesso em: 10 fev. 2021.
Portuguese 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
URI: https://repositorio.ufrn.br/handle/123456789/31459
Appears in Collections:ICe - Artigos publicados em periódicos

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