One-Sequence learning of human actions

Authors:
Carlos Orrite;Mario Rodríguez;Miguel Montañés
Affiliations:
I3A, University of Zaragoza, Spain;I3A, University of Zaragoza, Spain;I3A, University of Zaragoza, Spain
Venue:
HBU'11 Proceedings of the Second international conference on Human Behavior Unterstanding
Year:
2011

Citing 13
Cited 1

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Hidden Conditional Random Fields

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Actions as Space-Time Shapes

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Learning atomic human actions using variable-length Markov models

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One video is sufficient? Human activity recognition using active video composition

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Action Recognition from One Example

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Human behavior understanding for inducing behavioral change: application perspectives

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Abstract

In this paper we address the problem of human action recognition from a single training sequence per class using a modified version of the Hidden Markov Model. Inspired by codebook approaches in object and scene categorization, we first construct a codebook of possible discrete observations by applying a clustering algorithm to all samples from all classes. The number of clusters defines the size of the codebook. Given a new observation, we assign to it a probability to belong to every cluster, i.e., to correspond to a discrete value of the codebook. In this sense, we change the ‘winner takes all' rule in the discrete-observation HMM for a distributed probability of membership. It implies the modification of the Baum-Welch algorithm for training discrete HMM to be able to deal with fuzzy observations. We compare our approach with other models such as, dynamic time warping (DTW), continuous-observation HMM, Conditional Random Fields (CRF) and Hidden Conditional Random Fields (HCRF) for human action recognition.