Human activity learning and segmentation using partially hidden discriminative models


Autoria(s): Truyen, Tran The; Bui, Hung H.; Venkatesh, Svetha
Contribuinte(s)

[Unknown]

Data(s)

01/01/2005

Resumo

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.<br />

Identificador

http://hdl.handle.net/10536/DRO/DU:30044756

Idioma(s)

eng

Publicador

The Conference, HAREM 2005 in conjunction with BMVC 2005

Relação

http://dro.deakin.edu.au/eserv/DU:30044756/venkatesh-humanactivity-2005.pdf

Direitos

2005, The Authors

Palavras-Chave #patterns #low-level sensory data #hidden Markov models #conditional random field (CRF) #maximum entropy Markov model (MEMM)
Tipo

Conference Paper