Kinect and Episodic Reasoning for Human Action Recognition


Autoria(s): Cantarero, Ruben; Santofimia, Maria J.; Villa, David; Requena, Roberto; Campos, Maria; Florez-Revuelta, Francisco; Nebel, Jean-Christophe; Martinez del Rincon, Jesus; Lopez, Juan C.
Data(s)

01/06/2016

Identificador

http://pure.qub.ac.uk/portal/en/publications/kinect-and-episodic-reasoning-for-human-action-recognition(5c784922-1c2e-43f9-827d-6820a59fe79d).html

http://dx.doi.org/10.1007/978-3-319-40162-1_16

http://pure.qub.ac.uk/ws/files/89332291/main_3_.pdf

Idioma(s)

eng

Direitos

info:eu-repo/semantics/openAccess

Fonte

Cantarero , R , Santofimia , M J , Villa , D , Requena , R , Campos , M , Florez-Revuelta , F , Nebel , J-C , Martinez del Rincon , J & Lopez , J C 2016 , Kinect and Episodic Reasoning for Human Action Recognition . in Distributed Computing and Artificial Intelligence, 13th International Conference . Advances in Intelligent Systems and Computing , vol. 474 , pp. 147-154 , 13th International Conference on Distributed Computing and Artificial Intelligence (DCAI)) , Seville , Spain , 1-3 June . DOI: 10.1007/978-3-319-40162-1_16

Tipo

contributionToPeriodical

Resumo

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.

Formato

application/pdf