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MammalNet: A Large-scale Video Benchmark for Mammal Recognition and Behavior Understanding by Chen, Jun; Hu, Ming; Coker, Darren J.; Berumen, Michael L.; Costelloe, Blair; Beery, Sara; Rohrbach, Anna; Elhoseiny, Mohamed is a scholarly article available to read on EtoBox.

What is MammalNet: A Large-scale Video Benchmark for Mammal Recognition and Behavior Understanding about?

Monitoring animal behavior can facilitate conservation efforts by providing key insights into wildlife health, population status, and ecosystem function. Automatic recognition of animals and their behaviors is critical for capitalizing on the large unlabeled datasets generated by modern video devices and for accelerating monitoring efforts at scale. However, the development of automated recognition systems is currently hindered by a lack of appropriately labeled datasets. Existing video datasets 1) do not classify animals according to established biological taxonomies; 2) are too small to facilitate large-scale behavioral studies and are often limited to a single species; and 3) do not feature temporally localized annotations and therefore do not facilitate localization of targeted behaviors within longer video sequences. Thus, we propose MammalNet, a new large-scale animal behavior dataset with taxonomy-guided annotations of mammals and their common behaviors. MammalNet contains over 18K videos totaling 539 hours, which is ~10 times larger than the largest existing animal behavior dataset. It covers 17 orders, 69 families, and 173 mammal categories for animal categorization and ca

Author

Chen, Jun; Hu, Ming; Coker, Darren J.; Berumen, Michael L.; Costelloe, Blair; Beery, Sara; Rohrbach, Anna; Elhoseiny, Mohamed

Published

2023

Language

EN