Activity recognition and, more generally, behavior inference tasks are gaining a lot of interest. Much of it is work in the context of human behavior. New available tracking technologies for wild animals are generating datasets that indirectly may provide information about animal behavior. In this work, we propose a method for classifying these data into behavioral annotation, particularly collective behavior of a social group. Our method is based on sequence analysis with a direct encoding of the interactions of a group of wild animals. We evaluate our approach on a real world dataset, showing significant accuracy improvements over baseline methods.
@article{arxiv.1907.00932,
title = {A Framework For Identifying Group Behavior Of Wild Animals},
author = {Guido Muscioni and Riccardo Pressiani and Matteo Foglio and Margaret C. Crofoot and Marco D. Santambrogio and Tanya Berger-Wolf},
journal= {arXiv preprint arXiv:1907.00932},
year = {2019}
}
Comments
KDD19 Workshop on Data Mining and AI for Conservation, Earth Day (5 August 2019), Anchorage, AL