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We propose a novel computational method to extract information aboutinteractions among individuals with different behavioral states in a biologicalcollective from ordinary video recordings. Assuming that individuals are actingas finite state machines, our method first detects discrete behavioral statesof those individuals and then constructs a model of their state transitions,taking into account the positions and states of other individuals in thevicinity. We have tested the proposed method through applications to tworeal-world biological collectives: termites in an experimental setting andhuman pedestrians in a university campus. For each application, a robusttracking system was developed in-house, utilizing interactive humanintervention (for termite tracking) or online agent-based simulation (forpedestrian tracking). In both cases, significant interactions were detectedbetween nearby individuals with different states, demonstrating theeffectiveness of the proposed method.

Robust Tracking and Behavioral Modeling of Movements of Biological Collectives from Ordinary Video Recordings