Fish4Knowledge

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Fish4Knowledge

The Fish4Knowledge project (groups.inf.ed.ac.uk/f4k/) is pleased to announce the availability of 2 subsets of our tropical coral reeffish video and extracted fish data. See below for 3 other Fish4Knowledge project datasets.Dataset #1: a 10 minute video clip from all working cameras taken at 08:00 every day in the project Oct 1, 2010 - July 10, 2013, giving 5824 video clips (of 7-30 Mb). Note, some dates are missing. This data allows analysis of fish patterns over annual cycles and comparison between sites. About 2.5 M fish were detected.Dataset #2: 690 10 minute video clips from the 9 cameras taken from 06:00 - 19:00 on April 22, 2011 (out of 702 possible). This data allows analysis of fish patterns over a full day period and comparison between sites. About 16 M fish were detected.For each 10 minute video clip, there is also a CSV file that describes all fish ( of a sufficient size) that were detected in the video,including a boundary and a species identification. There were up to 24 species recognized.You might find the datasets useful for target detection in a tough undersea environment, multi-class recognition (where there are many false detections and mis-classifications), or even a bit of ecological analysis.You can find the data at:http://groups.inf.ed.ac.uk/f4k/F4KDATASAMPLES/INTERFACE/DATASAMPLES/search.phpUse of the data should include this citation:Bastiaan J. Boom, Jiyin He, Simone Palazzo, Phoenix X. Huang, Hsiu-Mei Chou,Fang-Pang Lin, Concetto Spampinato, Robert B. Fisher;A research tool for long-term and continuous analysis of fishassemblage in coral-reefs using underwater camera footage.Ecological Informatics, 2014, DOI: dx.doi.org/10.1016/j.ecoinf.2013.10.006.================Can I also mention 3 other Fish4Knowledge project ground-truthed datasets:* Underwater benchmark dataset for target detection against complex backgrounds* Fish species recognition* Trajectory-based fish behavior analysisSee: http://groups.inf.ed.ac.uk/f4k/GROUNDTRUTH/

Dataset Attributes

Label SVG
CategoriesAnimal, Fish, Video, Motion, Nature, Water, Camera