FishTrack23: An Ensemble Underwater Dataset for Multi-Object Tracking
Matthew Dawkins and Jack Prior and Bryon Lewis and Robin Faillettaz and Thompson Banez and Mary Salvi and Audrey Rollo and Julien Simon and Alexa Abanga and Matthew Campbell and Matthew Lucero and Aashish Chaudhary and Benjamin Richards and Anthony Hoogs

@inproceedings{dawkins2024fishtrack23,
title= {FishTrack23: An Ensemble Underwater Dataset for Multi-Object Tracking},
journal= {},
author= {Matthew Dawkins and Jack Prior and Bryon Lewis and Robin Faillettaz and Thompson Banez and Mary Salvi and Audrey Rollo and Julien Simon and Alexa Abanga and Matthew Campbell and Matthew Lucero and Aashish Chaudhary and Benjamin Richards and Anthony Hoogs},
booktitle= {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision},
pages= {7167--7176},
year= {2024},
url= {https://openaccess.thecvf.com/content/WACV2024/papers/Dawkins_FishTrack23_An_Ensemble_Underwater_Dataset_for_Multi-Object_Tracking_WACV_2024_paper.pdf},
abstract= {Tracking fish in optical underwater imagery contains a number of challenges not encountered in terrestrial domains. Video may contain large schools comprised of many individuals, dynamic natural backgrounds, variable target scales, volatile collection conditions, and non-fish moving confusors including debris, marine snow, and other organisms. Lastly, there is a lack of large public datasets for algorithm evaluation available in this domain. FishTrack aims to address these challenges by providing a large quantity of expert-annotated fish groundtruth tracks, in imagery and video collected across a range of different backgrounds, locations, collection conditions, and organizations.},
keywords= {NOAA, deep learning, Computer Vision, object detection, object classification, marine biology, IFREMER, Fish, Object Tracking, CDFW},
terms= {},
license= {CC-BY-4.0},
superseded= {}
}