Title: Robust Tracking and Behavioral Modeling of Movements of Biological Collectives from Ordinary Video Recordings,
Abstract: We propose a novel computational method to extract information about
interactions among individuals with different behavioral states in a biological
collective from ordinary video recordings. Assuming that individuals are acting
as finite state machines, our method first detects discrete behavioral states
of those individuals and then constructs a model of their state transitions,
taking into account the positions and states of other individuals in the
vicinity. We have tested the proposed method through applications to two
real-world biological collectives: termites in an experimental setting and
human pedestrians in a university campus. For each application, a robust
tracking system was developed in-house, utilizing interactive human
intervention (for termite tracking) or online agent-based simulation (for
pedestrian tracking). In both cases, significant interactions were detected
between nearby individuals with different states, demonstrating the
effectiveness of the proposed method.