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Human activity learning and segmentation using partially hidden discriminative models

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conference contribution
posted on 2005-01-01, 00:00 authored by T Truyen, H Bui, Svetha VenkateshSvetha Venkatesh
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.

History

Event

International Workshop on Human Activity Recognition and Modelling (2005 : Oxford, U. K.)

Pagination

87 - 95

Publisher

The Conference, HAREM 2005 in conjunction with BMVC 2005

Location

Oxford, U. K.

Place of publication

[Oxford, U. K.]

Start date

2005-09-09

Language

eng

Publication classification

E1.1 Full written paper - refereed

Copyright notice

2005, The Authors

Title of proceedings

HAREM 2005 : Proceedings of the International Workshop on Human Activity Recognition and Modelling

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