Video semantic analysis framework based on run-time production rules - Towards cognitive vision

Authors: Alejandro Zambrano Carlos Toro Marcos Nieto Doncel Ricardo Sotaquirá Cesar Sanín Edward Szczerbicki

Date: 25.07.2015

Journal of Universal Computer Science


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Abstract

This paper proposes a service-oriented architecture for video analysis which separates object detection from event recognition. Our aim is to introduce new tools to be considered in the pathway towards Cognitive Vision as a support for classical Computer Vision techniques that have been broadly used by the scientific community. In the article, we particularly focus in solving some of the reported scalability issues found in current Computer Vision approaches by introducing an experience based approximation based on the Set of Experience Knowledge Structure (SOEKS). In our proposal, object detection takes place clientside, while event recognition takes place server-side. In order to implement our approach, we introduce a novel architecture that aims at recognizing events defined by a user using production rules (a part of the SOEKS model) and the detections made by the client using their own algorithms for visual recognition. In order to test our methodology, we present a case study, showing the scalability enhancements provided.

BIB_text

@Article {
title = {Video semantic analysis framework based on run-time production rules - Towards cognitive vision},
journal = {Journal of Universal Computer Science},
pages = {865-870},
number = {6},
volume = {21},
keywds = {

Video analysis; Video event recognition; Video surveillance


}
abstract = {

This paper proposes a service-oriented architecture for video analysis which separates object detection from event recognition. Our aim is to introduce new tools to be considered in the pathway towards Cognitive Vision as a support for classical Computer Vision techniques that have been broadly used by the scientific community. In the article, we particularly focus in solving some of the reported scalability issues found in current Computer Vision approaches by introducing an experience based approximation based on the Set of Experience Knowledge Structure (SOEKS). In our proposal, object detection takes place clientside, while event recognition takes place server-side. In order to implement our approach, we introduce a novel architecture that aims at recognizing events defined by a user using production rules (a part of the SOEKS model) and the detections made by the client using their own algorithms for visual recognition. In order to test our methodology, we present a case study, showing the scalability enhancements provided.


}
isi = {1},
date = {2015-07-25},
year = {2015},
}
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