Advances in Stereo Vision

Stereopsis is a vision process whose geometrical foundation has been known for a long time, ever since the experiments by Wheatstone, in the 19th century. Nevertheless, its inner workings in biological organisms, as well as its emulation by computer systems, have proven elusive, and stereo vision remains a very active and challenging area of research nowadays. In this volume we have attempted to present a limited but relevant sample of the work being carried out in stereo vision, covering significant aspects both from the applied and from the theoretical standpoints

Advancements in Forest Fire Prevention: A Comprehensive Survey

Nowadays, the challenges related to technological and environmental development are becoming increasingly complex. Among the environmentally significant issues, wildfires pose a serious threat to the global ecosystem. The damages inflicted upon forests are manifold, leading not only to the destruction of terrestrial ecosystems but also to climate changes. Consequently, reducing their impact on both people and nature requires the adoption of effective approaches for prevention, early warning, and well-coordinated interventions. This document presents an analysis of the evolution of various technologies used in the detection, monitoring, and prevention of forest fires from past years to the present. It highlights the strengths, limitations, and future developments in this field. Forest fires have emerged as a critical environmental concern due to their devastating effects on ecosystems and the potential repercussions on the climate. Understanding the evolution of technology in addressing this issue is essential to formulate more effective strategies for mitigating and preventing wildfires

Advanced Map Matching Technologies and Techniques for Pedestrian/Wheelchair Navigation

Due to the constantly increasing technical advantages of mobile devices (such as smartphones), pedestrian/wheelchair navigation recently has achieved a high level of interest as one of smartphones’ potential mobile applications. While vehicle navigation systems have already reached a certain level of maturity, pedestrian/wheelchair navigation services are still in their infancy. By comparing vehicle navigation systems, a set of map matching requirements and challenges unique in pedestrian/wheelchair navigation is identified. To provide navigation assistance to pedestrians and wheelchair users, there is a need for the design and development of new map matching techniques. The main goal of this research is to investigate and develop advanced map matching technologies and techniques particular for pedestrian/wheelchair navigation services. As the first step in map matching, an adaptive candidate segment selection algorithm is developed to efficiently find candidate segments. Furthermore, to narrow down the search for the correct segment, advanced mathematical models are applied. GPS-based chain-code map matching, Hidden Markov Model (HMM) map matching, and fuzzy-logic map matching algorithms are developed to estimate real-time location of users in pedestrian/wheelchair navigation systems/services. Nevertheless, GPS signal is not always available in areas with high-rise buildings and even when there is a signal, the accuracy may not be high enough for localization of pedestrians and wheelchair users on sidewalks. To overcome these shortcomings of GPS, multi-sensor integrated map matching algorithms are investigated and developed in this research. These algorithms include a movement pattern recognition algorithm, using accelerometer and compass data, and a vision-based positioning algorithm to fill in signal gaps in GPS positioning. Experiments are conducted to evaluate the developed algorithms using real field test data (GPS coordinates and other sensors data). The experimental results show that the developed algorithms and the integrated sensors, i.e., a monocular visual odometry, a GPS, an accelerometer, and a compass, can provide high-quality and uninterrupted localization services in pedestrian/wheelchair navigation systems/services. The map matching techniques developed in this work can be applied to various pedestrian/wheelchair navigation applications, such as tracking senior citizens and children, or tourist service systems, and can be further utilized in building walking robots and automatic wheelchair navigation systems

Pattern Recognition

A wealth of advanced pattern recognition algorithms are emerging from the interdiscipline between technologies of effective visual features and the human-brain cognition process. Effective visual features are made possible through the rapid developments in appropriate sensor equipments, novel filter designs, and viable information processing architectures. While the understanding of human-brain cognition process broadens the way in which the computer can perform pattern recognition tasks. The present book is intended to collect representative researches around the globe focusing on low-level vision, filter design, features and image descriptors, data mining and analysis, and biologically inspired algorithms. The 27 chapters coved in this book disclose recent advances and new ideas in promoting the techniques, technology and applications of pattern recognition

Intelligent Sensors for Human Motion Analysis

The book, "Intelligent Sensors for Human Motion Analysis," contains 17 articles published in the Special Issue of the Sensors journal. These articles deal with many aspects related to the analysis of human movement. New techniques and methods for pose estimation, gait recognition, and fall detection have been proposed and verified. Some of them will trigger further research, and some may become the backbone of commercial systems

Autonomous navigation and mapping of mobile robots based on 2D/3D cameras combination

Aufgrund der tendenziell zunehmenden Nachfrage an Systemen zur Unterstützung des alltäglichen Lebens gibt es derzeit ein großes Interesse an autonomen Systemen. Autonome Systeme werden in Häusern, Büros, Museen sowie in Fabriken eingesetzt. Sie können verschiedene Aufgaben erledigen, beispielsweise beim Reinigen, als Helfer im Haushalt, im Bereich der Sicherheit und Bildung, im Supermarkt sowie im Empfang als Auskunft, weil sie dazu verwendet werden können, die Verarbeitungszeit zu kontrollieren und präzise, zuverlässige Ergebnisse zu liefern. Ein Forschungsgebiet autonomer Systeme ist die Navigation und Kartenerstellung. Das heißt, mobile Roboter sollen selbständig ihre Aufgaben erledigen und zugleich eine Karte der Umgebung erstellen, um navigieren zu können. Das Hauptproblem besteht darin, dass der mobile Roboter in einer unbekannten Umgebung, in der keine zusätzlichen Bezugsinformationen vorhanden sind, das Gelände erkunden und eine dreidimensionale Karte davon erstellen muss. Der Roboter muss seine Positionen innerhalb der Karte bestimmen. Es ist notwendig, ein unterscheidbares Objekt zu finden. Daher spielen die ausgewählten Sensoren und der Register-Algorithmus eine relevante Rolle. Die Sensoren, die sowohl Tiefen- als auch Bilddaten liefern können, sind noch unzureichend. Der neue 3D-Sensor, nämlich der "Photonic Mixer Device" (PMD), erzeugt mit hoher Bildwiederholfrequenz eine Echtzeitvolumenerfassung des umliegenden Szenarios und liefert Tiefen- und Graustufendaten. Allerdings erfordert die höhere Qualität der dreidimensionalen Erkundung der Umgebung Details und Strukturen der Oberflächen, die man nur mit einer hochauflösenden CCD-Kamera erhalten kann. Die vorliegende Arbeit präsentiert somit eine Exploration eines mobilen Roboters mit Hilfe der Kombination einer CCD- und PMD-Kamera, um eine dreidimensionale Karte der Umgebung zu erstellen. Außerdem wird ein Hochleistungsalgorithmus zur Erstellung von 3D Karten und zur Poseschätzung in Echtzeit unter Verwendung des "Simultaneous Localization and Mapping" (SLAM) Verfahrens präsentiert. Der autonom arbeitende, mobile Roboter soll ferner Aufgaben übernehmen, wie z.B. die Erkennung von Objekten in ihrer Umgebung, um verschiedene praktische Aufgaben zu lösen. Die visuellen Daten der CCD-Kamera liefern nicht nur eine hohe Auflösung der Textur-Daten für die Tiefendaten, sondern werden auch für die Objekterkennung verwendet. Der "Iterative Closest Point" (ICP) Algorithmus benutzt zwei Punktwolken, um den Bewegungsvektor zu bestimmen. Schließlich sind die Auswertung der Korrespondenzen und die Rekonstruktion der Karte, um die reale Umgebung abzubilden, in dieser Arbeit enthalten.Presently, intelligent autonomous systems have to perform very interesting tasks due to trendy increases in support demands of human living. Autonomous systems have been used in various applications like houses, offices, museums as well as in factories. They are able to operate in several kinds of applications such as cleaning, household assistance, transportation, security, education and shop assistance because they can be used to control the processing time, and to provide precise and reliable output. One research field of autonomous systems is mobile robot navigation and map generation. That means the mobile robot should work autonomously while generating a map, which the robot follows. The main issue is that the mobile robot has to explore an unknown environment and to generate a three dimensional map of an unknown environment in case that there is not any further reference information. The mobile robot has to estimate its position and pose. It is required to find distinguishable objects. Therefore, the selected sensors and registered algorithms are significant. The sensors, which can provide both, depth as well as image data are still deficient. A new 3D sensor, namely the Photonic Mixer Device (PMD), generates a high rate output in real-time capturing the surrounding scenario as well as the depth and gray scale data. However, a higher quality of three dimension explorations requires details and textures of surfaces, which can be obtained from a high resolution CCD camera. This work hence presents the mobile robot exploration using the integration of CCD and PMD camera in order to create a three dimensional map. In addition, a high performance algorithm for 3D mapping and pose estimation of the locomotion in real time, using the "Simultaneous Localization and Mapping" (SLAM) technique is proposed. The flawlessly mobile robot should also handle the tasks, such as the recognition of objects in its environment, in order to achieve various practical missions. Visual input from the CCD camera not only delivers high resolution texture data on depth volume, but is also used for object recognition. The “Iterative Closest Point” (ICP) algorithm is using two sets of points to find out the translation and rotation vector between two scans. Finally, the evaluation of the correspondences and the reconstruction of the map to resemble the real environment are included in this thesis

TractorEYE: Vision-based Real-time Detection for Autonomous Vehicles in Agriculture

Agricultural vehicles such as tractors and harvesters have for decades been able to navigate automatically and more efficiently using commercially available products such as auto-steering and tractor-guidance systems. However, a human operator is still required inside the vehicle to ensure the safety of vehicle and especially surroundings such as humans and animals. To get fully autonomous vehicles certified for farming, computer vision algorithms and sensor technologies must detect obstacles with equivalent or better than human-level performance. Furthermore, detections must run in real-time to allow vehicles to actuate and avoid collision.This thesis proposes a detection system (TractorEYE), a dataset (FieldSAFE), and procedures to fuse information from multiple sensor technologies to improve detection of obstacles and to generate a map. TractorEYE is a multi-sensor detection system for autonomous vehicles in agriculture. The multi-sensor system consists of three hardware synchronized and registered sensors (stereo camera, thermal camera and multi-beam lidar) mounted on/in a ruggedized and water-resistant casing. Algorithms have been developed to run a total of six detection algorithms (four for rgb camera, one for thermal camera and one for a Multi-beam lidar) and fuse detection information in a common format using either 3D positions or Inverse Sensor Models. A GPU powered computational platform is able to run detection algorithms online. For the rgb camera, a deep learning algorithm is proposed DeepAnomaly to perform real-time anomaly detection of distant, heavy occluded and unknown obstacles in agriculture. DeepAnomaly is -- compared to a state-of-the-art object detector Faster R-CNN -- for an agricultural use-case able to detect humans better and at longer ranges (45-90m) using a smaller memory footprint and 7.3-times faster processing. Low memory footprint and fast processing makes DeepAnomaly suitable for real-time applications running on an embedded GPU. FieldSAFE is a multi-modal dataset for detection of static and moving obstacles in agriculture. The dataset includes synchronized recordings from a rgb camera, stereo camera, thermal camera, 360-degree camera, lidar and radar. Precise localization and pose is provided using IMU and GPS. Ground truth of static and moving obstacles (humans, mannequin dolls, barrels, buildings, vehicles, and vegetation) are available as an annotated orthophoto and GPS coordinates for moving obstacles. Detection information from multiple detection algorithms and sensors are fused into a map using Inverse Sensor Models and occupancy grid maps. This thesis presented many scientific contribution and state-of-the-art within perception for autonomous tractors; this includes a dataset, sensor platform, detection algorithms and procedures to perform multi-sensor fusion. Furthermore, important engineering contributions to autonomous farming vehicles are presented such as easily applicable, open-source software packages and algorithms that have been demonstrated in an end-to-end real-time detection system. The contributions of this thesis have demonstrated, addressed and solved critical issues to utilize camera-based perception systems that are essential to make autonomous vehicles in agriculture a reality

Survey of computer vision algorithms and applications for unmanned aerial vehicles

This paper presents a complete review of computer vision algorithms and vision-based intelligent applications, that are developed in the field of the Unmanned Aerial Vehicles (UAVs) in the latest decade. During this time, the evolution of relevant technologies for UAVs; such as component miniaturization, the increase of computational capabilities, and the evolution of computer vision techniques have allowed an important advance in the development of UAVs technologies and applications. Particularly, computer vision technologies integrated in UAVs allow to develop cutting-edge technologies to cope with aerial perception difficulties; such as visual navigation algorithms, obstacle detection and avoidance and aerial decision-making. All these expert technologies have developed a wide spectrum of application for UAVs, beyond the classic military and defense purposes. Unmanned Aerial Vehicles and Computer Vision are common topics in expert systems, so thanks to the recent advances in perception technologies, modern intelligent applications are developed to enhance autonomous UAV positioning, or automatic algorithms to avoid aerial collisions, among others. Then, the presented survey is based on artificial perception applications that represent important advances in the latest years in the expert system field related to the Unmanned Aerial Vehicles. In this paper, the most significant advances in this field are presented, able to solve fundamental technical limitations; such as visual odometry, obstacle detection, mapping and localization, et cetera. Besides, they have been analyzed based on their capabilities and potential utility. Moreover, the applications and UAVs are divided and categorized according to different criteria.This research is supported by the Spanish Government through the CICYT projects (TRA2015-63708-R and TRA2013-48314-C3-1-R)

Recent Advances in Indoor Localization Systems and Technologies

Despite the enormous technical progress seen in the past few years, the maturity of indoor localization technologies has not yet reached the level of GNSS solutions. The 23 selected papers in this book present the recent advances and new developments in indoor localization systems and technologies, propose novel or improved methods with increased performance, provide insight into various aspects of quality control, and also introduce some unorthodox positioning methods