Research Article Episodic Reasoning for Vision-Based Human Action Recognition Maria J. Santofimia, 1 Jesus Martinez-del-Rincon, 2 and Jean-Christophe Nebel 3 1 Computer Architecture and Network Group, School of Computer Science, University of Castilla-La Mancha, 13072 Ciudad Real, Spain 2 Te Institute of Electronics, Communications and Information Technology (ECIT), Queens University of Belfast, Belfast BT3 9DT, UK 3 Digital Imaging Research Centre, Kingston University, London KT1 2EE, UK Correspondence should be addressed to Maria J. Santofmia; mariajose.santofmia@uclm.es Received 23 August 2013; Accepted 29 October 2013; Published 14 May 2014 Academic Editors: G. Bordogna and I. Garc´ ıa Copyright © 2014 Maria J. Santofmia et al. Tis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Smart Spaces, Ambient Intelligence, and Ambient Assisted Living are environmental paradigms that strongly depend on their capability to recognize human actions. While most solutions rest on sensor value interpretations and video analysis applications, few have realized the importance of incorporating common-sense capabilities to support the recognition process. Unfortunately, humanactionrecognitioncannotbesuccessfullyaccomplishedbyonlyanalyzingbodypostures.Onthecontrary,thistaskshould besupportedbyprofoundknowledgeofhumanagencynatureanditstightconnectiontothereasonsandmotivationsthatexplain it.Tecombinationofthisknowledgeandtheknowledgeabouthowtheworldworksisessentialforrecognizingandunderstanding human actions without committing common-senseless mistakes. Tis work demonstrates the impact that episodic reasoning has in improving the accuracy of a computer vision system for human action recognition. Tis work also presents formalization, implementation, and evaluation details of the knowledge model that supports the episodic reasoning. 1. Introduction Recognizing human actions is an essential requirement for fulflling the vision of Smart Spaces, Ambient Intelligence, or Ambient Assisted Living. Tese paradigms envision envi- ronments in which electronic devices, merged with the background, operate as sensors retrieving environmental information. Among all diferent types of sensors, video cameras are extremely powerful devices because of the great amount of contextual information that they are capable of capturing. However, despite human’s ability to understand efortlessly video sequences through observation, computer vision systems still have work to do in this regard. Automatic video understanding is a delicate task that yet remains an unresolved topic [1]. Among all the challenges involved in video understanding, this paper focuses on human action recognition since this is an enabling key for Smart Spaces applications. Applications that depend on the identifcation of certain behavior require the ability to recognizeactions.Forexample,kickingandpunchingaretwo actions that suggest an ongoing fght. In this sense, having theabilitytorecognizethesequenceofactionsthatdefnean undesirable behavior can be used to trigger a security alarm. Obviously, several challenges arise when dealing with human action recognition. In addition to the inherent difculty of recognizing diferent people’s body postures performingthesameaction[2],diferentactionsmayinvolve similar or identical poses. Moreover, images recorded within a real environment are not always captured from the best perspective or angle, which makes it impossible to retrieve poses consistently [3]. Fortunately, the human ability to recognize actions does not only rely on visual analysis of human body postures but also requires additional sources of information such as context, knowledge about actor intentions, or knowledge about how the world works normally referred to as common sense. Tis type of information helps people to recognize, among several similar actions, the one that is the most Hindawi Publishing Corporation e Scientific World Journal Volume 2014, Article ID 270171, 18 pages http://dx.doi.org/10.1155/2014/270171