idTracker: tracking individuals in a group by automatic identification of unmarked animals

By A. Pérez-Escudero, Julián Vicente-Page, R. Hinz, S. Arganda, G. D. de Polavieja. Nature Methods, 2014

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BEEtag: A Low-Cost, Image-Based Tracking System for the Study of Animal Behavior and Locomotion (2015, background): “[4,14,15], with notable exceptions such as [7]), individual tracking depends on information from previous frames, and therefore when an individual is either (a) not tracked or (b) incorrectly tracked in one or a few frames (i.”
Fast animal pose estimation using deep neural networks (2018, methodology): “Lastly, through the addition of a segmentation step for analyzing videos of multiple animals 3,14,46 , LEAP can potentially estimate poses for multiple interacting individuals.”
Deep learning for smart fish farming: applications, opportunities and challenges (2020, background): “Finally, idTracker and further developments in identification algorithms for unmarked animals have been successful for 2~15 individuals in small groups (Pérez-Escudero et al., 2014).”
ToxTrac: A fast and robust software for tracking organisms (2017, methodology): “F IGURE 2 Example of occlusions handled by ToxTrac from the zebrafish dataset (Pérez-Escudero et al., 2014)”