Visual surveillance for collective behavior analysis: From human to fish

Abstract

Animals including human being are often with others in groups such as people crowds, fish schools, and flocks of birds. In such groups, they are interacting with each other to tell their intentions. By analyzing such collective behavior, we can obtain plenty of information not only for understanding the ecology of animals but also for practical applications including aquaculture and social safety. Hence, collective behavior analysis has been an active research area of both ecology and engineering. In this paper, we will introduce our recent research projects for visual surveillance of collective behavior analysis. First, we will present small object detection based on a deep neural network. The method is specially designed for detecting small swimming fish. Although training deep neural network usually requires a large number of annotated data, our proposed method reduces the cost of manual annotation. Next, we will present social group detection based on multiple instance learning. The multiple instance learning enables us to extract meaningful information from given data. We examined both of the proposed method using actual data to show their performance.