A Review and Analysis of Eye-Gaze Estimation Systems, Algorithms and Performance Evaluation Methods in Consumer Platforms

In this paper a review is presented of the research on eye gaze estimation techniques and applications, that has progressed in diverse ways over the past two decades. Several generic eye gaze use-cases are identified: desktop, TV, head-mounted, automotive and handheld devices. Analysis of the literature leads to the identification of several platform specific factors that influence gaze tracking accuracy. A key outcome from this review is the realization of a need to develop standardized methodologies for performance evaluation of gaze tracking systems and achieve consistency in their specification and comparative evaluation. To address this need, the concept of a methodological framework for practical evaluation of different gaze tracking systems is proposed.Comment: 25 pages, 13 figures, Accepted for publication in IEEE Access in July 201

Human Attention Assessment Using A Machine Learning Approach with GAN-based Data Augmentation Technique Trained Using a Custom Dataset

Human–robot interactions require the ability of the system to determine if the user is paying attention. However, to train such systems, massive amounts of data are required. In this study, we addressed the issue of data scarcity by constructing a large dataset (containing ~120,000 photographs) for the attention detection task. Then, by using this dataset, we established a powerful baseline system. In addition, we extended the proposed system by adding an auxiliary face detection module and introducing a unique GAN-based data augmentation technique. Experimental results revealed that the proposed system yields superior performance compared to baseline models and achieves an accuracy of 88% on the test set. Finally, we created a web application for testing the proposed model in real time

Driver Attention based on Deep Learning for a Smart Vehicle to Driver (V2D) Interaction

La atención del conductor es un tópico interesante dentro del mundo de los vehículos inteligentes para la consecución de tareas que van desde la monitorización del conductor hasta la conducción autónoma. Esta tesis aborda este tópico basándose en algoritmos de aprendizaje profundo para conseguir una interacción inteligente entre el vehículo y el conductor. La monitorización del conductor requiere una estimación precisa de su mirada en un entorno 3D para conocer el estado de su atención. En esta tesis se aborda este problema usando una única cámara, para que pueda ser utilizada en aplicaciones reales, sin un alto coste y sin molestar al conductor. La herramienta desarrollada ha sido evaluada en una base de datos pública (DADA2000), obteniendo unos resultados similares a los obtenidos mediante un seguidor de ojos caro que no puede ser usado en un vehículo real. Además, ha sido usada en una aplicación que evalúa la atención del conductor en la transición de modo autónomo a manual de forma simulada, proponiendo el uso de una métrica novedosa para conocer el estado de la situación del conductor en base a su atención sobre los diferentes objetos de la escena. Por otro lado, se ha propuesto un algoritmo de estimación de atención del conductor, utilizando las últimas técnicas de aprendizaje profundo como son las conditional Generative Adversarial Networks (cGANs) y el Multi-Head Self-Attention. Esto permite enfatizar ciertas zonas de la escena al igual que lo haría un humano. El modelo ha sido entrenado y validado en dos bases de datos públicas (BDD-A y DADA2000) superando a otras propuestas del estado del arte y consiguiendo unos tiempos de inferencia que permiten su uso en aplicaciones reales. Por último, se ha desarrollado un modelo que aprovecha nuestro algoritmo de atención del conductor para comprender una escena de tráfico obteniendo la decisión tomada por el vehículo y su explicación, en base a las imágenes tomadas por una cámara situada en la parte frontal del vehículo. Ha sido entrenado en una base de datos pública (BDD-OIA) proponiendo un modelo que entiende la secuencia temporal de los eventos usando un Transformer Encoder, consiguiendo superar a otras propuestas del estado del arte. Además de su validación en la base de datos, ha sido implementado en una aplicación que interacciona con el conductor aconsejando sobre las decisiones a tomar y sus explicaciones ante diferentes casos de uso en un entorno simulado. Esta tesis explora y demuestra los beneficios de la atención del conductor para el mundo de los vehículos inteligentes, logrando una interacción vehículo conductor a través de las últimas técnicas de aprendizaje profundo

マシンビジョンを用いた農業圃場における操縦者安全のための機械学習システムの開発

この博士論文は内容の要約のみの公開（または一部非公開）になっています筑波大学 (University of Tsukuba)201

A Context Aware Classification System for Monitoring Driver’s Distraction Levels

Understanding the safety measures regarding developing self-driving futuristic cars is a concern for decision-makers, civil society, consumer groups, and manufacturers. The researchers are trying to thoroughly test and simulate various driving contexts to make these cars fully secure for road users. Including the vehicle’ surroundings offer an ideal way to monitor context-aware situations and incorporate the various hazards. In this regard, different studies have analysed drivers’ behaviour under different case scenarios and scrutinised the external environment to obtain a holistic view of vehicles and the environment. Studies showed that the primary cause of road accidents is driver distraction, and there is a thin line that separates the transition from careless to dangerous. While there has been a significant improvement in advanced driver assistance systems, the current measures neither detect the severity of the distraction levels nor the context-aware, which can aid in preventing accidents. Also, no compact study provides a complete model for transitioning control from the driver to the vehicle when a high degree of distraction is detected. The current study proposes a context-aware severity model to detect safety issues related to driver’s distractions, considering the physiological attributes, the activities, and context-aware situations such as environment and vehicle. Thereby, a novel three-phase Fast Recurrent Convolutional Neural Network (Fast-RCNN) architecture addresses the physiological attributes. Secondly, a novel two-tier FRCNN-LSTM framework is devised to classify the severity of driver distraction. Thirdly, a Dynamic Bayesian Network (DBN) for the prediction of driver distraction. The study further proposes the Multiclass Driver Distraction Risk Assessment (MDDRA) model, which can be adopted in a context-aware driving distraction scenario. Finally, a 3-way hybrid CNN-DBN-LSTM multiclass degree of driver distraction according to severity level is developed. In addition, a Hidden Markov Driver Distraction Severity Model (HMDDSM) for the transitioning of control from the driver to the vehicle when a high degree of distraction is detected. This work tests and evaluates the proposed models using the multi-view TeleFOT naturalistic driving study data and the American University of Cairo dataset (AUCD). The evaluation of the developed models was performed using cross-correlation, hybrid cross-correlations, K-Folds validation. The results show that the technique effectively learns and adopts safety measures related to the severity of driver distraction. In addition, the results also show that while a driver is in a dangerous distraction state, the control can be shifted from driver to vehicle in a systematic manner

Designing biomimetic vehicle-to-pedestrian communication protocols for autonomously operating & parking on-road electric vehicles

Thesis (S.M.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 125-127).With research institutions from various private, government and academic sectors performing research into autonomous vehicle deployment strategies, the way we think about vehicles must adapt. But what happens when the driver, the main conduit of information transaction between the vehicle and its surroundings, is removed? The EVITA system aims to fill this communication void by giving the autonomous vehicle the means to sense others around it, and react to various stimuli in as intuitive ways as possible by taking design cues from the living world. The system is comprised of various types of sensors (computer vision, UWB beacon tracking, sonar) and actuators (light, sound, mechanical) in order to express recognition of others, announcement of intentions, and portraying the vehicle's general state. All systems are built on the 2 nd version of the 1/2 -scale CityCar concept vehicle, featuring advanced mixed-materials (CFRP + Aluminum) and a significantly more modularized architecture.by Nicholas Pennycooke.S.M

Applications de la vision omnidirectionnelle à la perception de scènes pour des systèmes mobiles

Ce mémoire présente une synthèse des travaux que j’ai menés à l’ESIGELEC au sein de son institut de recherche l’IRSEEM. Mes activités de recherche ont porté dans un premier temps sur la conception et l’évaluation de dispositifs de mesure de la dynamique de la marche de personnes atteintes de pathologies de la hanche, dans le cadre de ma thèse effectuée à l’université de Rouen en lien le Centre Hospitalo-Universitaire de Rouen. En 2003, j’ai rejoint les équipes de recherche qui se constituaient avec la mise sur pieds de l’IRSEEM, Institut de Recherche en Systèmes Electroniques Embarqués, créé en 2001. Dans ce laboratoire, j’ai structuré et développé une activité de recherche dans le domaine de la vision par ordinateur appliquée au véhicule intelligent et à la robotique mobile autonome. Dans un premier temps, j’ai concentré mes travaux à l’étude de systèmes de vision omnidirectionnelle tels que les capteurs catadioptriques centraux et leur utilisation pour des applications mobiles embarquées ou débarquées : modélisation et calibrage, reconstruction tridimensionnelle de scènes par stéréovision et déplacement du capteur. Dans un second temps, je me suis intéressé à la conception et la mise en œuvre de systèmes de vision à projection non centrale (capteurs catadioptriques à miroirs composés, caméra plénoptique). Ces travaux ont été effectués au travers en collaboration avec le MIS de l‘Université Picardie Jules Verne et l’ISIR de l’Université Pierre et Marie Curie. Enfin, dans le cadre d’un programme de recherche en collaboration avec l’Université du Kent, j’ai consacré une partie de mes travaux à l’adaptation de méthodes de traitement d’images et de classification pour la détection de visages sur images omnidirectionnelles (adaptation du détecteur de Viola et Jones) et à la reconnaissance biométrique d’une personne par analyse de sa marche. Aujourd’hui, mon activité s’inscrit dans le prolongement du renforcement des projets de l’IRSEEM dans le domaine de la robotique mobile et du véhicule autonome : mise en place d’un plateau de mesures pour la navigation autonome, coordination de projets de recherche en prise avec les besoins industriels. Mes perspectives de recherche ont pour objet l’étude de nouvelles solutions pour la perception du mouvement et la localisation en environnement extérieur et sur les méthodes et moyens nécessaires pour objectiver la performance et la robustesse de ces solutions sur des scénarios réalistes

NASA Tech Briefs, June 1994

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