Parallel Computational Intelligence-Based Multi-Camera Surveillance System

In this work, we present a multi-camera surveillance system based on the use of self-organizing neural networks to represent events on video. The system processes several tasks in parallel using GPUs (graphic processor units). It addresses multiple vision tasks at various levels, such as segmentation, representation or characterization, analysis and monitoring of the movement. These features allow the construction of a robust representation of the environment and interpret the behavior of mobile agents in the scene. It is also necessary to integrate the vision module into a global system that operates in a complex environment by receiving images from multiple acquisition devices at video frequency. Offering relevant information to higher level systems, monitoring and making decisions in real time, it must accomplish a set of requirements, such as: time constraints, high availability, robustness, high processing speed and re-configurability. We have built a system able to represent and analyze the motion in video acquired by a multi-camera network and to process multi-source data in parallel on a multi-GPU architecture.

A three-dimensional representation method for noisy point clouds based on growing self-organizing maps accelerated on GPUs

The research described in this thesis was motivated by the need of a robust model capable of representing 3D data obtained with 3D sensors, which are inherently noisy. In addition, time constraints have to be considered as these sensors are capable of providing a 3D data stream in real time. This thesis proposed the use of Self-Organizing Maps (SOMs) as a 3D representation model. In particular, we proposed the use of the Growing Neural Gas (GNG) network, which has been successfully used for clustering, pattern recognition and topology representation of multi-dimensional data. Until now, Self-Organizing Maps have been primarily computed offline and their application in 3D data has mainly focused on free noise models, without considering time constraints. It is proposed a hardware implementation leveraging the computing power of modern GPUs, which takes advantage of a new paradigm coined as General-Purpose Computing on Graphics Processing Units (GPGPU). The proposed methods were applied to different problem and applications in the area of computer vision such as the recognition and localization of objects, visual surveillance or 3D reconstruction.

Inconsistencies in Guidelines for Visual Health Surveillance of VDT Workers

Objectives: In Europe, 25% of workers use video display terminals (VDTs). Occupational health surveillance has been considered a key element in the protection of these workers. Nevertheless, it is unclear if guidelines available for this purpose, based on EU standards and available evidence, meet currently accepted quality criteria. The aim of this study was to appraise three sets of European VDT guidelines (UK, France, Spain) in which regulatory and evidence-based approaches for visual health have been formulated and recommendations for practice made. Methods: Three independent appraisers used an adapted AGREE instrument with seven domains to appraise the guidelines. A modified nominal group technique approach was used in two consecutive phases: first, individual evaluation of the three guidelines simultaneously, and second, a face-to-face meeting of appraisers to discuss scoring. Analysis of ratings obtained in each domain and variability among appraisers was undertaken (correlation and kappa coefficients). Results: All guidelines had low domain scores. The domain evaluated most highly was Scope and purpose, while Applicability was scored minimally. The UK guidelines had the highest overall score, and the Spanish ones had the lowest. The analysis of reliability and differences between scores in each domain showed a high level of agreement. Conclusions: These results suggest current guidelines used in these countries need an update. The formulation of evidence-base European guidelines on VDT could help to reduce the significant variation of national guidelines, which may have an impact on practical application.