Kinect and Episodic Reasoning for Human Action Recognition

Computer Architecture and Networks Group, University of Castilla-La Mancha, Ciudad Real, Spain

Ruben.Cantarero@uclm.es

Computer Architecture and Networks Group, University of Castilla-La Mancha, Ciudad Real, Spain

MariaJose.Santofimia@uclm.es

Computer Architecture and Networks Group, University of Castilla-La Mancha, Ciudad Real, Spain

David.Villa@uclm.es

Computer Architecture and Networks Group, University of Castilla-La Mancha, Ciudad Real, Spain

JuanCarlos.Lopez@uclm.es

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  • CONFERENCE — International Symposium on Distributed Computing and Artificial Intelligence

  • DATE — 01/06/2016 – 03/06/2016

  • PUBLISHER  Springer

  • YEAR  2016

  • ISBN  978-3-319-40162-1

  • DOI  10.1007/978-3-319-40162-1_16

Cantarero, R., Santofimia, M. J., Villa, D., Requena, R., Campos, M., Florez-Revuelta, F., … & Lopez, J. C. (2016). Kinect and episodic reasoning for human action recognition. In Distributed Computing and Artificial Intelligence, 13th International Conference (pp. 147-154). Springer, Cham.

@inproceedings{cantarero2016kinect,
title={Kinect and episodic reasoning for human action recognition},
author={Cantarero, Ruben and Santofimia, Maria J and Villa, David and Requena, Roberto and Campos, Maria and Florez-Revuelta, Francisco and Nebel, Jean-Christophe and Martinez-del-Rincon, Jesus and Lopez, Juan C},
booktitle={Distributed Computing and Artificial Intelligence, 13th International Conference},
pages={147--154},
year={2016},
organization={Springer}
}

Abstract

This paper presents a method for rational behaviour recognition that combines vision-based pose estimation with knowledge modeling and reasoning. The proposed method consists of two stages. First, RGB-D images are used in the estimation of the body postures. Then, estimated actions are evaluated to verify that they make sense. This method requires rational behaviour to be exhibited. To comply with this requirement, this work proposes a rational RGB-D dataset with two types of sequences, some for training and some for testing. Preliminary results show the addition of knowledge modeling and reasoning leads to a significant increase of recognition accuracy when compared to a system based only on computer vision.

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