FishTrack22: An Ensemble Dataset for Multi-Object Tracking Evaluation
Type | Poster | ||||||||
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Date | 2022-06 | ||||||||
Language | English | ||||||||
Other localization | https://www.cv4animals.com/ | ||||||||
Author(s) | Dawkins Matt1, Campbell Matthiew2, Prior Jack2, Faillettaz Robin3, Simon Julien3, Lucero Matthew4, Banez Thompson4, Richards Benjamin2, Rollo Audrey2, Salvi Mary1, Lewis Byron1, Davis Brandon1, Blue Rusty1, Hoogs Anthony1, Chaudhary Aashish1 | ||||||||
Affiliation(s) | 1 : Kitware Inc, USA 2 : NOAA Fisheries, USA 3 : Ifremer, France 4 : California Department of Fish and Wildlife, USA |
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Meeting | CV4Animals: Computer Vision for Animal Behavior Tracking and Modeling. June 19-24 2022, New Orleans, Louisiana, USA | ||||||||
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 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. |
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