Action recognition with global features

Authors:
Arash Mokhber;Catherine Achard;Xingtai Qu;Maurice Milgram
Affiliations:
Laboratoire des Instruments et Systèmes d'Ile de France (LISIF), Université Pierre et Marie Curie, Paris;Laboratoire des Instruments et Systèmes d'Ile de France (LISIF), Université Pierre et Marie Curie, Paris;Laboratoire des Instruments et Systèmes d'Ile de France (LISIF), Université Pierre et Marie Curie, Paris;Laboratoire des Instruments et Systèmes d'Ile de France (LISIF), Université Pierre et Marie Curie, Paris
Venue:
ICCV'05 Proceedings of the 2005 international conference on Computer Vision in Human-Computer Interaction
Year:
2005

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Abstract

In this study, a new method allowing recognizing and segmenting everyday life actions is proposed. Only one camera is utilized without calibration. Viewpoint invariance is obtained by several acquisitions of the same action. To enhance robustness, each sequence is characterized globally: a detection of moving areas is first computed on each image. All these binary points form a volume in the three-dimensional (3D) space (x,y,t). This volume is characterized by its geometric 3D moments. Action recognition is then carried out by computing the Mahalanobis distance between the vector of features of the action to be recognized and those of the reference database. Results, which validate the suggested approach, are presented on a base of 1662 sequences performed by several persons and categorized in eight actions. An extension of the method for the segmentation of sequences with several actions is also proposed.