Action recognition with semi-global characteristics and hidden Markov models

Authors:
Catherine Achard;Xingtai Qu;Arash Mokhber;Maurice Milgram
Affiliations:
Institut des Systèmes Intelligents et Robotique, Université Pierre et Marie Curie, Paris Cedex, Heidelberg, Germany;Institut des Systèmes Intelligents et Robotique, Université Pierre et Marie Curie, Paris Cedex, Heidelberg, Germany;Institut des Systèmes Intelligents et Robotique, Université Pierre et Marie Curie, Paris Cedex, Heidelberg, Germany;Institut des Systèmes Intelligents et Robotique, Université Pierre et Marie Curie, Paris Cedex, Heidelberg, Germany
Venue:
ACIVS'07 Proceedings of the 9th international conference on Advanced concepts for intelligent vision systems
Year:
2007

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Abstract

In this article, a new approach is presented for action recognition with only one non-calibrated camera. Invariance to view point is obtained with several acquisitions of the same action. The originality of the presented approach consists of characterizing sequences by a temporal succession of semi-global features, which are extracted from "space-time micro-volumes". The advantages of the proposed approach is the use of robust features (estimated on several frames) associated to the ability to manage actions with variable duration and to easily segment the sequences with algorithms that are specific to time varying data. For the recognition, each view of each action is modeled by an Hidden Markov Model system. Results presented on 1614 sequences of everyday life actions like "walking", "sitting down", "bending down", performed by several persons validate the proposed approach.