Abnormal Event Recognition: A Hybrid Approach Using SemanticWeb Technologies
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.
Cite
Text
Greco et al. "Abnormal Event Recognition: A Hybrid Approach Using SemanticWeb Technologies." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2016. doi:10.1109/CVPRW.2016.164Markdown
[Greco et al. "Abnormal Event Recognition: A Hybrid Approach Using SemanticWeb Technologies." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2016.](https://mlanthology.org/cvprw/2016/greco2016cvprw-abnormal/) doi:10.1109/CVPRW.2016.164BibTeX
@inproceedings{greco2016cvprw-abnormal,
title = {{Abnormal Event Recognition: A Hybrid Approach Using SemanticWeb Technologies}},
author = {Greco, Luca and Ritrovato, Pierluigi and Saggese, Alessia and Vento, Mario},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
year = {2016},
pages = {1297-1304},
doi = {10.1109/CVPRW.2016.164},
url = {https://mlanthology.org/cvprw/2016/greco2016cvprw-abnormal/}
}