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Alexandros Andre Chaaraoui
Researcher at University of Alicante
Publications - 26
Citations - 1175
Alexandros Andre Chaaraoui is an academic researcher from University of Alicante. The author has contributed to research in topics: Feature (machine learning) & Silhouette. The author has an hindex of 15, co-authored 26 publications receiving 1015 citations. Previous affiliations of Alexandros Andre Chaaraoui include Google.
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Journal ArticleDOI
A review on vision techniques applied to Human Behaviour Analysis for Ambient-Assisted Living
TL;DR: A review on HBA for AAL and ageing in place purposes focusing specially on vision techniques and useful tools and datasets are analysed in order to provide help for initiating projects.
Journal ArticleDOI
Silhouette-based human action recognition using sequences of key poses
TL;DR: A human action recognition method is presented in which pose representation is based on the contour points of the human silhouette and actions are learned by making use of sequences of multi-view key poses, achieving state-of-the-art success rates without compromising the speed of the recognition process.
Journal ArticleDOI
Visual privacy protection methods
TL;DR: This paper seeks to clarify how privacy can be protected in imagery data, so as a main contribution a comprehensive classification of the protection methods for visual privacy as well as an up-to-date review of them are provided.
Journal ArticleDOI
Evolutionary joint selection to improve human action recognition with RGB-D devices
Alexandros Andre Chaaraoui,José Ramón Padilla-López,Pau Climent-Pérez,Francisco Flórez-Revuelta +3 more
TL;DR: An evolutionary algorithm is used to determine the optimal subset of skeleton joints, taking into account the topological structure of the skeleton, in order to improve the final success rate.
Proceedings ArticleDOI
Fusion of Skeletal and Silhouette-Based Features for Human Action Recognition with RGB-D Devices
TL;DR: The combination of body pose estimation and 2D shape, in order to provide additional characteristic value, is considered so as to improve human action recognition and achieves to improve the recognition rates, outperforming state-of-the-art results in recognition rate and robustness.