FishTrack22: An Ensemble Dataset for Multi-Object Tracking Evaluation

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 public datasets for algorithm evaluation available in this domain. FishTrack22 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. Approximately 1 million bounding boxes across 45k tracks are included in the release of the ensemble, with potential for future growth in later releases.