HMM based action recognition with projection histogram features

Authors:
Roberto Vezzani;Davide Baltieri;Rita Cucchiara
Affiliations:
Dipartimento di Ingegneria dell'Informazione, University of Modena and Reggio, Emilia, Modena, Italy;Dipartimento di Ingegneria dell'Informazione, University of Modena and Reggio, Emilia, Modena, Italy;Dipartimento di Ingegneria dell'Informazione, University of Modena and Reggio, Emilia, Modena, Italy
Venue:
ICPR'10 Proceedings of the 20th International conference on Recognizing patterns in signals, speech, images, and videos
Year:
2010

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Abstract

Hidden Markov Models (HMM) have been widely used for action recognition, since they allow to easily model the temporal evolution of a single or a set of numeric features extracted from the data. The selection of the feature set and the related emission probability function are the key issues to be defined. In particular, if the training set is not sufficiently large, a manual or automatic feature selection and reduction is mandatory. In this paper we propose to model the emission probability function as a Mixture of Gaussian and the feature set is obtained from the projection histograms of the foreground mask. The projection histograms contain the number of moving pixel for each row and for each column of the frame and they provide sufficient information to infer the instantaneous posture of the person. Then, the HMM framework recovers the temporal evolution of the postures recognizing in such a manner the global action. The proposed method have been successfully tested on the UT-Tower and on the Weizmann Datasets.