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Learning and understanding the typical patterns in the daily activities androutines of people from low-level sensory data is an important problem in manyapplication domains such as building smart environments, or providingintelligent assistance. Traditional approaches to this problem typically relyon supervised learning and generative models such as the hidden Markov modelsand its extensions. While activity data can be readily acquired from pervasivesensors, e.g. in smart environments, providing manual labels to supportsupervised training is often extremely expensive. In this paper, we propose anew approach based on semi-supervised training of partially hiddendiscriminative models such as the conditional random field (CRF) and themaximum entropy Markov model (MEMM). We show that these models allow us toincorporate 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 thesemodels can perform better than their generative counterpart, the partiallyhidden Markov model, even when a substantial amount of labels are unavailable.

Human Activity Learning and Segmentation using Partially Hidden Discriminative Models