Automatic Identification of Bee Movement

Identifying and recording animal movements is a critical, but time-consuming step in behavior research. The task is especially onerous in studies involving social insects because of the number of animals that must be observed simultaneously. To address this, we present a system that can automatically analyze animal movement, and label it, on the basis of examples provided by a human expert. For our experiments, activity in an arena is recorded on video, that video is converted into location information for each animal by a vision-based tracker, and then numerical features such as velocity and heading change are extracted. The features are used in turn to label the sequence of movements for each observed animal. Our approach uses a combination of k-nearest neighbor classification and hidden Markov model techniques. The system was evaluated on several hundred honey bee trajectories extracted from a 15 minute video of activity in an observation hive. Movements were labeled by hand, and also labeled by our system. Our system was able to label movements with 81.5% accuracy in a fraction of the
time it would take a human.