Robust Tracking and Behavioral Modeling of Movements of Biological Collectives from Ordinary Video Recordings
This work addresses the challenge of modeling behavioral interactions in biological collectives for researchers in biology and computer vision, though it is incremental as it builds on existing tracking and state machine concepts.
The authors tackled the problem of extracting interaction information among individuals in biological collectives from ordinary video recordings, achieving significant detection of interactions between nearby individuals with different states in termites and human pedestrians.
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.