An Interval-Based Bayesian Generative Model for Human Complex Activity Recognition

TL;DR

This paper introduces a probabilistic Bayesian model using interval relations and latent variables to recognize complex human activities, capturing diverse styles and temporal dependencies effectively.

Contribution

It presents a novel interval-based Bayesian generative model with latent variables for complex activity recognition, capable of learning network structures from data.

Findings

Effective recognition of complex activities demonstrated on benchmark datasets.

Model captures diverse activity styles and maintains temporal consistency.

Constructed a large, publicly available dataset of complex hand activities.

Abstract

Complex activity recognition is challenging due to the inherent uncertainty and diversity of performing a complex activity. Normally, each instance of a complex activity has its own configuration of atomic actions and their temporal dependencies. We propose in this paper an atomic action-based Bayesian model that constructs Allen's interval relation networks to characterize complex activities with structural varieties in a probabilistic generative way: By introducing latent variables from the Chinese restaurant process, our approach is able to capture all possible styles of a particular complex activity as a unique set of distributions over atomic actions and relations. We also show that local temporal dependencies can be retained and are globally consistent in the resulting interval network. Moreover, network structure can be learned from empirical data. A new dataset of complex hand activities has been constructed and made publicly available, which is much larger in size than any existing datasets. Empirical evaluations on benchmark datasets as well as our in-house dataset demonstrate the competitiveness of our approach.