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

TL;DR

This paper introduces a new computational approach to analyze interactions and behavioral states in biological collectives from ordinary videos, using finite state machine modeling and robust tracking.

Contribution

It presents a novel method for extracting behavioral states and modeling their transitions in biological groups from standard video recordings.

Findings

Effective detection of behavioral states in termites and pedestrians.

Identification of significant interactions between individuals with different states.

Robust tracking system developed for real-world applications.

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.