Les travaux de recherche développés dans cette thèse concernent la mise en oeuvre d'un système de vidéo surveillance intelligente en milieu hospitalier. Dans le contexte d'une application en unité de soins intensifs médicale, nous introduisons la notion originale de Boite Noire Médicale et nous proposons un nouveau système de monitoring visuel de Détection Automatique de Situations à risque et d'Alerte (DASA) basé sur un système de vidéosurveillance multi-caméra intelligent. L'objectif étant d'interpréter les flux d'informations visuelles et de détecter en temps réel les situations à risque afin de prévenir l'équipe médicale et ensuite archiver les évènements dans une base de donnée vidéo qui représente la Boite Noire Médicale. Le système d'interprétation est basé sur des algorithmes de reconnaissance de scénarios qui exploitent les Modèles de Markovs Cachés (MMCs). Une extension du modèle MMC standard est proposé afin de gérer la structure hiérarchique interne des scénarios et de contrôler la durée de chaque état du modèle markovien. La contribution majeure de ce travail repose sur l'intégration d'un raisonnement de type évènementiel, pour gérer la décision de reconnaissance en tenant compte des imperfections des informations disponibles. Les techniques de reconnaissance de scénarios proposées ont été testées et évaluées sur une base de séquences vidéo médicales et comparés aux modèles de Markov cachés probabilistiques classiques. / This thesis focuses on the study and the implementation of an intelligent visual monitoring system in hospitals. In the context of an application for patient monitoring in mediacal intensive care unit, we introduce an original concept of the Medical Black Box and we propose a new system for visual monitoring of Automatic Detection of risk Situations and Alert (DASA) based on a CCTV system with network smart camera. The aim is to interpret the visual information flow and to detect at real-time risk situations to prevent the mediacl team and then archive the events in a video that is based Medical Black Box data. The interpretation system is based on scenario recognition algorithms that exploit the Hidden Markov Models (HMM). An extension of the classic model of HMM is proposed to handle the internal reporting structure of the scenarios and to control the duration of each state of the Markov model. The main contribution of this work relies on the integration of an evidential reasoning, in order to manage the recognition decision taking into account the imperfections of available information. The proposed scenarios recognition method have been tested and assessed on database of medical video sequences and compared to standard probabilistic Hidden Markov Models.

This research investigates wireless intrusion detection techniques for detecting attacks on IEEE 802.11i Robust Secure Networks (RSNs). Despite using a variety of comprehensive preventative security measures, the RSNs remain vulnerable to a number of attacks. Failure of preventative measures to address all RSN vulnerabilities dictates the need for a comprehensive monitoring capability to detect all attacks on RSNs and also to proactively address potential security vulnerabilities by detecting security policy violations in the WLAN. This research proposes novel wireless intrusion detection techniques to address these monitoring requirements and also studies correlation of the generated alarms across wireless intrusion detection system (WIDS) sensors and the detection techniques themselves for greater reliability and robustness. The specific outcomes of this research are: A comprehensive review of the outstanding vulnerabilities and attacks in IEEE 802.11i RSNs. A comprehensive review of the wireless intrusion detection techniques currently available for detecting attacks on RSNs. Identification of the drawbacks and limitations of the currently available wireless intrusion detection techniques in detecting attacks on RSNs. Development of three novel wireless intrusion detection techniques for detecting RSN attacks and security policy violations in RSNs. Development of algorithms for each novel intrusion detection technique to correlate alarms across distributed sensors of a WIDS. Development of an algorithm for automatic attack scenario detection using cross detection technique correlation. Development of an algorithm to automatically assign priority to the detected attack scenario using cross detection technique correlation.