A Novel Prediction Method for Early Recognition of Global Human Behaviour in Image Sequences

Please use this identifier to cite or link to this item: http://hdl.handle.net/10045/53708
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Title: A Novel Prediction Method for Early Recognition of Global Human Behaviour in Image Sequences
Authors: Azorin-Lopez, Jorge | Saval-Calvo, Marcelo | Fuster-Guilló, Andrés | Garcia-Rodriguez, Jose
Research Group/s: Informática Industrial y Redes de Computadores
Center, Department or Service: Universidad de Alicante. Departamento de Tecnología Informática y Computación
Keywords: Human behaviour recognition | Early detection | Activity representation | Neural networks | Computer vision
Knowledge Area: Arquitectura y Tecnología de Computadores
Issue Date: Apr-2016
Publisher: Springer Science+Business Media New York
Citation: Neural Processing Letters. 2016, 43(2): 363-387. doi:10.1007/s11063-015-9412-y
Abstract: Human behaviour recognition has been, and still remains, a challenging problem that involves different areas of computational intelligence. The automated understanding of people activities from video sequences is an open research topic in which the computer vision and pattern recognition areas have made big efforts. In this paper, the problem is studied from a prediction point of view. We propose a novel method able to early detect behaviour using a small portion of the input, in addition to the capabilities of it to predict behaviour from new inputs. Specifically, we propose a predictive method based on a simple representation of trajectories of a person in the scene which allows a high level understanding of the global human behaviour. The representation of the trajectory is used as a descriptor of the activity of the individual. The descriptors are used as a cue of a classification stage for pattern recognition purposes. Classifiers are trained using the trajectory representation of the complete sequence. However, partial sequences are processed to evaluate the early prediction capabilities having a specific observation time of the scene. The experiments have been carried out using the three different dataset of the CAVIAR database taken into account the behaviour of an individual. Additionally, different classic classifiers have been used for experimentation in order to evaluate the robustness of the proposal. Results confirm the high accuracy of the proposal on the early recognition of people behaviours.
Sponsor: This work was supported in part by the University of Alicante, Valencian Government and Spanish government under grants GRE11-01, GV/2013/005 and DPI2013-40534-R.
URI: http://hdl.handle.net/10045/53708
ISSN: 1370-4621 (Print) | 1573-773X (Online)
DOI: 10.1007/s11063-015-9412-y
Language: eng
Type: info:eu-repo/semantics/article
Rights: © Springer Science+Business Media New York 2015. The final publication is available at Springer via http://dx.doi.org/10.1007/s11063-015-9412-y
Peer Review: si
Publisher version: http://dx.doi.org/10.1007/s11063-015-9412-y
Appears in Collections:INV - I2RC - Artículos de Revistas
INV - AIA - Artículos de Revistas

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