  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.
