Latent Hierarchical Model for Activity Recognition

TL;DR

This paper introduces a hierarchical model for human activity recognition that jointly predicts actions and activities, capturing rich contextual information efficiently through a latent layer within a linear-chain structure.

Contribution

It proposes a unified framework with a latent hierarchical layer for activity recognition, enabling exact inference and efficient learning without manual latent state labeling.

Findings

Outperforms state-of-the-art methods on benchmark datasets

Achieves higher accuracy in activity recognition

Demonstrates computational efficiency in inference and learning

Abstract

We present a novel hierarchical model for human activity recognition. In contrast to approaches that successively recognize actions and activities, our approach jointly models actions and activities in a unified framework, and their labels are simultaneously predicted. The model is embedded with a latent layer that is able to capture a richer class of contextual information in both state-state and observation-state pairs. Although loops are present in the model, the model has an overall linear-chain structure, where the exact inference is tractable. Therefore, the model is very efficient in both inference and learning. The parameters of the graphical model are learned with a Structured Support Vector Machine (Structured-SVM). A data-driven approach is used to initialize the latent variables; therefore, no manual labeling for the latent states is required. The experimental results from using two benchmark datasets show that our model outperforms the state-of-the-art approach, and our model is computationally more efficient.