Human Activity Learning and Segmentation using Partially Hidden Discriminative Models

TL;DR

This paper introduces a semi-supervised learning approach using partially hidden discriminative models like CRFs and MEMMs for activity recognition, outperforming traditional generative models especially with limited labeled data.

Contribution

It presents a novel semi-supervised training method for discriminative models in activity segmentation, reducing reliance on manual labels and improving accuracy over generative models.

Findings

Discriminative models outperform generative models with limited labels.

Semi-supervised training effectively incorporates unlabeled data.

Experimental results show improved activity recognition accuracy.

Abstract

Learning and understanding the typical patterns in the daily activities and routines of people from low-level sensory data is an important problem in many application domains such as building smart environments, or providing intelligent assistance. Traditional approaches to this problem typically rely on supervised learning and generative models such as the hidden Markov models and its extensions. While activity data can be readily acquired from pervasive sensors, e.g. in smart environments, providing manual labels to support supervised training is often extremely expensive. In this paper, we propose a new approach based on semi-supervised training of partially hidden discriminative models such as the conditional random field (CRF) and the maximum entropy Markov model (MEMM). We show that these models allow us to incorporate both labeled and unlabeled data for learning, and at the same time, provide us with the flexibility and accuracy of the discriminative framework. Our experimental results in the video surveillance domain illustrate that these models can perform better than their generative counterpart, the partially hidden Markov model, even when a substantial amount of labels are unavailable.