Abnormal Event Recognition: A Hybrid Approach Using Semantic Web

Luca Greco, Pierluigi Ritrovato, Alessia Saggese, Mario Vento; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2016, pp. 58-65

Abstract

Video surveillance systems generated about 65% of the Universe Big Data in 2015. The development of systems for intelligent analysis of such a large amount of data is among the most investigated topics in the academia and commercial world. Recent outcomes in knowledge management and computational intelligence demonstrate the effectiveness of semantic technologies in several fields like image and text analysis, hand writing and speech recognition. In this paper a solution that, starting from the output of a people tracking algorithm, is able to recognize simple events (person falling to the ground) and complex ones (person aggression) is presented. The proposed solution uses semantic web technologies for automatically annotating the output produced by the tracking algorithm; a sets of rules for reasoning on these annotated data are also proposed. Such rules allow to define complex analytics functions demonstrating the effectiveness of hybrid approaches for event recognition.

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[bibtex]
@InProceedings{Greco_2016_CVPR_Workshops,
author = {Greco, Luca and Ritrovato, Pierluigi and Saggese, Alessia and Vento, Mario},
title = {Abnormal Event Recognition: A Hybrid Approach Using Semantic Web},
booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2016}
}