Automated audiovisual behavior recognition in wild primates

The field of ethology seeks to understand animal behavior from both mechanistic and functional perspectives and to identify the various genetic, developmental, ecological, and social drivers of behavioral variation in the wild (1). It is increasingly becoming a data-rich science: Technological advances in data collection, including biologgers, camera traps, and audio recorders, now allow us to capture animal behavior in an unprecedented level of detail (2). In particular, large data archives including both audio and visual information have immense potential to measure individual- and population-level variation as well as ontogenetic and cultural changes in behavior that may span large temporal and spatial scales. However, this potential often goes untapped: The training and human effort required to process large volumes of video data continue to limit the scale and depth at which behavior can be analyzed. Automating the measurement of behavior can transform ethological research, open up large-scale video archives for detailed interrogation, and be a powerful tool to monitor and protect threatened species in the wild. With rapid advances in deep learning, the novel field of computational ethology is quickly emerging at the intersection of computer science, engineering, and biology, using computer vision algorithms to process large volumes of data (3).