2019/07/01 by Guido Muscioni, Muscioni, Guido, Riccardo Pressiani +9
Computer Science · Psychology · Social Sciences · #Computer Vision and Pattern Recognition (cs.CV) #Evolutionary Game Theory and Cooperation #FOS: Computer and information sciences #Machine Learning (cs.LG) #Music and Audio Processing #Primate Behavior and Ecology
paper · pdf · doi:10.48550/arxiv.1907.00932
openalex publication_date 2019/07/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.