Bayesian Inference of Recursive Sequences of Group Activities from Tracks

TL;DR

This paper introduces a probabilistic model that infers multi-level, recursive group activities from individual trajectories, capturing hierarchical structures, roles, and recursive behaviors in complex scenarios.

Contribution

It presents a novel Bayesian generative model with an MCMC inference framework for analyzing recursive, hierarchical group activities from trajectory data.

Findings

Effective in simulated scenarios

Successfully applied to VIRAT and UCLA datasets

Captures hierarchical and recursive activity structures

Abstract

We present a probabilistic generative model for inferring a description of coordinated, recursively structured group activities at multiple levels of temporal granularity based on observations of individuals' trajectories. The model accommodates: (1) hierarchically structured groups, (2) activities that are temporally and compositionally recursive, (3) component roles assigning different subactivity dynamics to subgroups of participants, and (4) a nonparametric Gaussian Process model of trajectories. We present an MCMC sampling framework for performing joint inference over recursive activity descriptions and assignment of trajectories to groups, integrating out continuous parameters. We demonstrate the model's expressive power in several simulated and complex real-world scenarios from the VIRAT and UCLA Aerial Event video data sets.