We describe a clustering-based algorithm for tracking a dynamically varying number of targets observed by multiple sensors. The algorithm relies on discrete target detections (e.g., laser "hits") and a simple model of the targets to be tracked (e.g. a human is modeled in 2-D as a circle). The algorithm is evaluated in the context of a 4 versus 4 basketball game (8 targets) using 4 SICK LMS291 laser scanners as input. Our evaluations show that the sensor system correctly reports the number of targets roughly 99% of the time. We also demonstrate use of the tracker with two video datasets of multiple changing numbers of ants and fish, respectively

Our understanding of social insect behavior has significantly influenced artificial intelligence (AI) and multirobot systems' research (e.g., ant algorithms and swarm robotics). In this work, however, we focus on the opposite question: "How can multirobot systems research contribute to the understanding of social animal behavior?" As we show, we are able to contribute at several levels. First, using algorithms that originated in the robotics community, we can track animals under observation to provide essential quantitative data for animal behavior research. Second, by developing and applying algorithms originating in speech recognition and computer vision, we can automatically label the behavior of animals under observation. In some cases the automatic labeling is more accurate and consistent than manual behavior identification. Our ultimate goal, however, is to automatically create, from observation, executable models of behavior. An executable model is a control program for an agent that can run in simulation (or on a robot). The representation for these executable models is drawn from research in multirobot systems programming. In this paper we present the algorithms we have developed for tracking, recognizing, and learning models of social animal behavior, details of their implementation, and quantitative experimental results using them to study social insects