A Framework For Identifying Group Behavior Of Wild Animals
This work addresses behavior inference for wildlife researchers, but it is incremental as it applies sequence analysis to a new domain.
The paper tackles the problem of classifying collective behavior in wild animal groups from tracking data, achieving significant accuracy improvements over baseline methods.
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.