A Framework For Identifying Group Behavior Of Wild Animals
Politecnico di Milano（米兰理工大学）；University of California, Davis（加州大学戴维斯分校）；University of Illinois at Chicago（伊利诺伊大学芝加哥分校）
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
