Unobtrusive and pervasive video-based eye-gaze tracking

Eye-gaze tracking has long been considered a desktop technology that finds its use inside the traditional office setting, where the operating conditions may be controlled. Nonetheless, recent advancements in mobile technology and a growing interest in capturing natural human behaviour have motivated an emerging interest in tracking eye movements within unconstrained real-life conditions, referred to as pervasive eye-gaze tracking. This critical review focuses on emerging passive and unobtrusive video-based eye-gaze tracking methods in recent literature, with the aim to identify different research avenues that are being followed in response to the challenges of pervasive eye-gaze tracking. Different eye-gaze tracking approaches are discussed in order to bring out their strengths and weaknesses, and to identify any limitations, within the context of pervasive eye-gaze tracking, that have yet to be considered by the computer vision community.peer-reviewe

Visual Servoing

The goal of this book is to introduce the visional application by excellent researchers in the world currently and offer the knowledge that can also be applied to another field widely. This book collects the main studies about machine vision currently in the world, and has a powerful persuasion in the applications employed in the machine vision. The contents, which demonstrate that the machine vision theory, are realized in different field. For the beginner, it is easy to understand the development in the vision servoing. For engineer, professor and researcher, they can study and learn the chapters, and then employ another application method

Fast, collaborative acquisition of multi-view face images using a camera network and its impact on real-time human identification

Biometric systems have been typically designed to operate under controlled environments based on previously acquired photographs and videos. But recent terror attacks, security threats and intrusion attempts have necessitated a transition to modern biometric systems that can identify humans in real-time under unconstrained environments. Distributed camera networks are appropriate for unconstrained scenarios because they can provide multiple views of a scene, thus offering tolerance against variable pose of a human subject and possible occlusions. In dynamic environments, the face images are continually arriving at the base station with different quality, pose and resolution. Designing a fusion strategy poses significant challenges. Such a scenario demands that only the relevant information is processed and the verdict (match / no match) regarding a particular subject is quickly (yet accurately) released so that more number of subjects in the scene can be evaluated.;To address these, we designed a wireless data acquisition system that is capable of acquiring multi-view faces accurately and at a rapid rate. The idea of epipolar geometry is exploited to get high multi-view face detection rates. Face images are labeled to their corresponding poses and are transmitted to the base station. To evaluate the impact of face images acquired using our real-time face image acquisition system on the overall recognition accuracy, we interface it with a face matching subsystem and thus create a prototype real-time multi-view face recognition system. For front face matching, we use the commercial PittPatt software. For non-frontal matching, we use a Local binary Pattern based classifier. Matching scores obtained from both frontal and non-frontal face images are fused for final classification. Our results show significant improvement in recognition accuracy, especially when the front face images are of low resolution

Intelligent surveillance of indoor environments based on computer vision and 3D point cloud fusion

A real-time detection algorithm for intelligent surveillance is presented. The system, based on 3D change detection with respect to a complex scene model, allows intruder monitoring and detection of added and missing objects, under different illumination conditions. The proposed system has two independent stages. First, a mapping application provides an accurate 3D wide model of the scene, using a view registration approach. This registration is based on computer vision and 3D point cloud. Fusion of visual features with 3D descriptors is used in order to identify corresponding points in two consecutive views. The matching of these two views is first estimated by a pre-alignment stage, based on the tilt movement of the sensor, later they are accurately aligned by an Iterative Closest Point variant (Levenberg-Marquardt ICP), which performance has been improved by a previous filter based on geometrical assumptions. The second stage provides accurate intruder and object detection by means of a 3D change detection approach, based on Octree volumetric representation, followed by a clusters analysis. The whole scene is continuously scanned, and every captured is compared with the corresponding part of the wide model thanks to the previous analysis of the sensor movement parameters. With this purpose a tilt-axis calibration method has been developed. Tests performed show the reliable performance of the system under real conditions and the improvements provided by each stage independently. Moreover, the main goal of this application has been enhanced, for reliable intruder detection by the tilting of the sensors using its built-in motor to increase the size of the monitored area. (C) 2015 Elsevier Ltd. All rights reserved.This work was supported by the Spanish Government through the CICYT projects (TRA2013-48314-C3-1-R) and (TRA2011-29454-C03-02)

Object Tracking in Distributed Video Networks Using Multi-Dimentional Signatures

From being an expensive toy in the hands of governmental agencies, computers have evolved a long way from the huge vacuum tube-based machines to today\u27s small but more than thousand times powerful personal computers. Computers have long been investigated as the foundation for an artificial vision system. The computer vision discipline has seen a rapid development over the past few decades from rudimentary motion detection systems to complex modekbased object motion analyzing algorithms. Our work is one such improvement over previous algorithms developed for the purpose of object motion analysis in video feeds. Our work is based on the principle of multi-dimensional object signatures. Object signatures are constructed from individual attributes extracted through video processing. While past work has proceeded on similar lines, the lack of a comprehensive object definition model severely restricts the application of such algorithms to controlled situations. In conditions with varying external factors, such algorithms perform less efficiently due to inherent assumptions of constancy of attribute values. Our approach assumes a variable environment where the attribute values recorded of an object are deemed prone to variability. The variations in the accuracy in object attribute values has been addressed by incorporating weights for each attribute that vary according to local conditions at a sensor location. This ensures that attribute values with higher accuracy can be accorded more credibility in the object matching process. Variations in attribute values (such as surface color of the object) were also addressed by means of applying error corrections such as shadow elimination from the detected object profile. Experiments were conducted to verify our hypothesis. The results established the validity of our approach as higher matching accuracy was obtained with our multi-dimensional approach than with a single-attribute based comparison

Real-time acquisition of multi-view face images to support robust face recognition using a wireless camera network

Recent terror attacks, intrusion attempts and criminal activities have necessitated a transition to modern biometric systems that are capable of identifying suspects in real time. But real-time biometrics is challenging given the computationally intensive nature of video processing and the potential occlusions and variations in pose of a subject in an unconstrained environment. The objective of this dissertation is to utilize the robustness and parallel computational abilities of a distributed camera network for fast and robust face recognition.;In order to support face recognition using a camera network, a collaborative middle-ware service is designed that enables the rapid extraction of multi-view face images of multiple subjects moving through a region. This service exploits the epipolar geometry between cameras to speed up multi view face detection rates. By quickly detecting face images within the network, labeling the pose of each face image, filtering them based on their suitability of recognition and transmitting only the resultant images to a base station for recognition, both the required network bandwidth and centralized processing overhead are reduced. The performance of the face image acquisition system is evaluated using an embedded camera network that is deployed in indoor environments that mimic walkways in public places. The relevance of the acquired images for recognition is evaluated by using a commercial software for matching acquired probe images. The experimental results demonstrate significant improvement in face recognition system performance over traditional systems as well as increase in multi-view face detection rate over purely image processing based approaches

Sensor fusion in smart camera networks for ambient intelligence

This short report introduces the topics of PhD research that was conducted on 2008-2013 and was defended on July 2013. The PhD thesis covers sensor fusion theory, gathers it into a framework with design rules for fusion-friendly design of vision networks, and elaborates on the rules through fusion experiments performed with four distinct applications of Ambient Intelligence

Reconstrução tridimensional de ambientes usando LIDAR e câmara

Tridimensional reconstruction is still a challenging area, that has multiple application in architecture and robotics. Several technologies are used today, like Stereoscopy or Structured Light, however, none is able to achieve precise geometric results, which are usually required. A technology, LiDAR, has evolved as the de facto technology for tridimensional reconstruction, being able to achieve unmatched results. Yet, this technology is unable to register the color of objects, so the usual solution is to use a camera for this. Therefore, in this work, a set of algorithms and techniques for tridimensional reconstruction with a LiDAR laser scanner and a camera was developed. Moreover, a 3D scanner was developed to register real-word scenes. In particular, an innovative calibration method was developed to calibrate the laser scanner, which performed above a similar calibration method. Finally, the reconstruction process was tested with real data. The geometric reconstruction was very accurate but the color reconstruction was not, especially because of the poor calibration of the camera.Reconstrução tridimensional é uma área com múltiplas aplicações em arquitetura e robótica. Inúmera tecnologias existem para este efeito, como por exemplo a estereoscopia e luz estruturada. Contudo, muitas tecnologias carecem de precisão geométrica, que é por vezes um requisito. Outra tecnologia - LiDAR - é usada por causa dos seus resultados geométricos inigualáveis. No entanto, LiDAR é incapaz de capturar a cor dos objetos e uma solução é a integração de uma câmara com o LiDAR. Assim, neste trabalho foram desenvolvidos um conjunto de técnicas e algoritmos direcionados para a reconstrução 3D com LiDAR e câmara. Além disso, um scanner 3D foi desenvolvido para registrar cenas reais. Em particular, um método de calibração inovador foi desenvolvido para a calibração do laser, com precisão superior a um método semelhante. Finalmente, os métodos foram testados com dados de cenas reais. A reconstrução geométrica foi bem sucedida mas o registo de cor ficou aquém do que era esperado, por causa de uma calibração pouco precisa da câmara.Mestrado em Engenharia Mecânic