MusA: Using Indoor Positioning and Navigation to Enhance Cultural Experiences in a museum

In recent years there has been a growing interest into the use of multimedia mobile guides in museum environments. Mobile devices have the capabilities to detect the user context and to provide pieces of information suitable to help visitors discovering and following the logical and emotional connections that develop during the visit. In this scenario, location based services (LBS) currently represent an asset, and the choice of the technology to determine users' position, combined with the definition of methods that can effectively convey information, become key issues in the design process. In this work, we present MusA (Museum Assistant), a general framework for the development of multimedia interactive guides for mobile devices. Its main feature is a vision-based indoor positioning system that allows the provision of several LBS, from way-finding to the contextualized communication of cultural contents, aimed at providing a meaningful exploration of exhibits according to visitors' personal interest and curiosity. Starting from the thorough description of the system architecture, the article presents the implementation of two mobile guides, developed to respectively address adults and children, and discusses the evaluation of the user experience and the visitors' appreciation of these application

Smart environment monitoring through micro unmanned aerial vehicles

In recent years, the improvements of small-scale Unmanned Aerial Vehicles (UAVs) in terms of flight time, automatic control, and remote transmission are promoting the development of a wide range of practical applications. In aerial video surveillance, the monitoring of broad areas still has many challenges due to the achievement of different tasks in real-time, including mosaicking, change detection, and object detection. In this thesis work, a small-scale UAV based vision system to maintain regular surveillance over target areas is proposed. The system works in two modes. The first mode allows to monitor an area of interest by performing several flights. During the first flight, it creates an incremental geo-referenced mosaic of an area of interest and classifies all the known elements (e.g., persons) found on the ground by an improved Faster R-CNN architecture previously trained. In subsequent reconnaissance flights, the system searches for any changes (e.g., disappearance of persons) that may occur in the mosaic by a histogram equalization and RGB-Local Binary Pattern (RGB-LBP) based algorithm. If present, the mosaic is updated. The second mode, allows to perform a real-time classification by using, again, our improved Faster R-CNN model, useful for time-critical operations. Thanks to different design features, the system works in real-time and performs mosaicking and change detection tasks at low-altitude, thus allowing the classification even of small objects. The proposed system was tested by using the whole set of challenging video sequences contained in the UAV Mosaicking and Change Detection (UMCD) dataset and other public datasets. The evaluation of the system by well-known performance metrics has shown remarkable results in terms of mosaic creation and updating, as well as in terms of change detection and object detection

The Exploitation of Data from Remote and Human Sensors for Environment Monitoring in the SMAT Project

In this paper, we outline the functionalities of a system that integrates and controls a fleet of Unmanned Aircraft Vehicles (UAVs). UAVs have a set of payload sensors employed for territorial surveillance, whose outputs are stored in the system and analysed by the data exploitation functions at different levels. In particular, we detail the second level data exploitation function whose aim is to improve the sensors data interpretation in the post-mission activities. It is concerned with the mosaicking of the aerial images and the cartography enrichment by human sensors—the social media users. We also describe the software architecture for the development of a mash-up (the integration of information and functionalities coming from the Web) and the possibility of using human sensors in the monitoring of the territory, a field in which, traditionally, the involved sensors were only the hardware ones.JRC.H.6-Digital Earth and Reference Dat

Using Linear Features for Aerial Image Sequence Mosaiking

With recent advances in sensor technology and digital image processing techniques, automatic image mosaicking has received increased attention in a variety of geospatial applications, ranging from panorama generation and video surveillance to image based rendering. The geometric transformation used to link images in a mosaic is the subject of image orientation, a fundamental photogrammetric task that represents a major research area in digital image analysis. It involves the determination of the parameters that express the location and pose of a camera at the time it captured an image. In aerial applications the typical parameters comprise two translations (along the x and y coordinates) and one rotation (rotation about the z axis). Orientation typically proceeds by extracting from an image control points, i.e. points with known coordinates. Salient points such as road intersections, and building corners are commonly used to perform this task. However, such points may contain minimal information other than their radiometric uniqueness, and, more importantly, in some areas they may be impossible to obtain (e.g. in rural and arid areas). To overcome this problem we introduce an alternative approach that uses linear features such as roads and rivers for image mosaicking. Such features are identified and matched to their counterparts in overlapping imagery. Our matching approach uses critical points (e.g. breakpoints) of linear features and the information conveyed by them (e.g. local curvature values and distance metrics) to match two such features and orient the images in which they are depicted. In this manner we orient overlapping images by comparing breakpoint representations of complete or partial linear features depicted in them. By considering broader feature metrics (instead of single points) in our matching scheme we aim to eliminate the effect of erroneous point matches in image mosaicking. Our approach does not require prior approximate parameters, which are typically an essential requirement for successful convergence of point matching schemes. Furthermore, we show that large rotation variations about the z-axis may be recovered. With the acquired orientation parameters, image sequences are mosaicked. Experiments with synthetic aerial image sequences are included in this thesis to demonstrate the performance of our approach

Traffic Surveillance and Automated Data Extraction from Aerial Video Using Computer Vision, Artificial Intelligence, and Probabilistic Approaches

In transportation engineering, sufficient, reliable, and diverse traffic data is necessary for effective planning, operations, research, and professional practice. Using aerial imagery to achieve traffic surveillance and collect traffic data is one of the feasible ways that is facilitated by the advances of technologies in many related areas. A great deal of aerial imagery datasets are currently available and more datasets are collected every day for various applications. It will be beneficial to make full and efficient use of the attribute rich imagery as a resource for valid and useful traffic data for many applications in transportation research and practice. In this dissertation, a traffic surveillance system that can collect valid and useful traffic data using quality-limited aerial imagery datasets with diverse characteristics is developed. Two novel approaches, which can achieve robust and accurate performance, are proposed and implemented for this system. The first one is a computer vision-based approach, which uses convolutional neural network (CNN) to detect vehicles in aerial imagery and uses features to track those detections. This approach is capable of detecting and tracking vehicles in the aerial imagery datasets with a very limited quality. Experimental results indicate the performance of this approach is very promising and it can achieve accurate measurements for macroscopic traffic data and is also potential for reliable microscopic traffic data. The second approach is a multiple hypothesis tracking (MHT) approach with innovative kinematics and appearance models (KAM). The implemented MHT module is designed to cooperate with the CNN module in order to extend and improve the vehicle tracking system. Experiments are designed based on a meticulously established synthetic vehicle detection datasets, originally induced scale-agonistic property of MHT, and comprehensively identified metrics for performance evaluation. The experimental results not only indicate that the performance of this approach can be very promising, but also provide solutions for some long-standing problems and reveal the impacts of frame rate, detection noise, and traffic configurations as well as the effects of vehicle appearance information on the performance. The experimental results of both approaches prove the feasibility of traffic surveillance and data collection by detecting and tracking vehicles in aerial video, and indicate the direction of further research as well as solutions to achieve satisfactory performance with existing aerial imagery datasets that have very limited quality and frame rates. This traffic surveillance system has the potential to be transformational in how large area traffic data is collected in the future. Such a system will be capable of achieving wide area traffic surveillance and extracting valid and useful traffic data from wide area aerial video captured with a single platfor

The ORSER LANDSAT Data Base of Pennsylvania

A mosaicked LANDSAT data base for Pennsylvania, installed at the computation center of the Pennsylvania State University is described. Initially constructed by Penn State's Office for Remote Sensing of Earth Resources (ORSER) for the purpose of assisting in state-wide mapping of gypsy moth defoliation, the data base will be available to a variety of potential users. It will provide geometrically correct LANDSAT data accessible by political, jurisdictional, or arbitrary boundaries

Image Station Matching, Preprocessing, Spatial Registration and Change Detection with Multi-Temporal Remotely-Sensed Imagery

A method for collecting and processing remotely sensed imagery in order to achieve precise spatial co-registration (e.g., matched alignment) between multi-temporal image sets is presented. Such precise alignment or spatial co-registration of imagery can be used for change detection, image fusion, and temporal analysis/modeling. Further, images collected in this manner may be further processed in such a way that image frames or line arrays from corresponding photo stations are matched, co-aligned and if desired merged into a single image and/or subjected to the same processing sequence. A second methodology for automated detection of moving objects within a scene using a time series of remotely sensed imagery is also presented. Specialized image collection and preprocessing procedures are utilized to obtain precise spatial co-registration (image registration) between multitemporal image frame sets. In addition, specialized change detection techniques are employed in order to automate the detection of moving objects

A Novel GAN-Based Anomaly Detection and Localization Method for Aerial Video Surveillance at Low Altitude

The last two decades have seen an incessant growth in the use of Unmanned Aerial Vehicles (UAVs) equipped with HD cameras for developing aerial vision-based systems to support civilian and military tasks, including land monitoring, change detection, and object classification. To perform most of these tasks, the artificial intelligence algorithms usually need to know, a priori, what to look for, identify. or recognize. Actually, in most operational scenarios, such as war zones or post-disaster situations, areas and objects of interest are not decidable a priori since their shape and visual features may have been altered by events or even intentionally disguised (e.g., improvised explosive devices (IEDs)). For these reasons, in recent years, more and more research groups are investigating the design of original anomaly detection methods, which, in short, are focused on detecting samples that differ from the others in terms of visual appearance and occurrences with respect to a given environment. In this paper, we present a novel two-branch Generative Adversarial Network (GAN)-based method for low-altitude RGB aerial video surveillance to detect and localize anomalies. We have chosen to focus on the low-altitude sequences as we are interested in complex operational scenarios where even a small object or device can represent a reason for danger or attention. The proposed model was tested on the UAV Mosaicking and Change Detection (UMCD) dataset, a one-of-a-kind collection of challenging videos whose sequences were acquired between 6 and 15 m above sea level on three types of ground (i.e., urban, dirt, and countryside). Results demonstrated the effectiveness of the model in terms of Area Under the Receiving Operating Curve (AUROC) and Structural Similarity Index (SSIM), achieving an average of 97.2% and 95.7%, respectively, thus suggesting that the system can be deployed in real-world applications

Monitorización 3D de cultivos y cartografía de malas hierbas mediante vehículos aéreos no tripulados para un uso sostenible de fitosanitarios

En esta Tesis Doctoral se han utilizado las imágenes procedentes de un UAV para abordar la sostenibilidad de la aplicación de productos fitosanitarios mediante la generación de mapas que permitan su aplicación localizada. Se han desarrollado dos formas diferentes y complementarias para lograr este objetivo: 1) la reducción de la aplicación de herbicidas en post-emergencia temprana mediante el diseño de tratamientos dirigidos a las zonas infestadas por malas hierbas en varios cultivos herbáceos; y 2) la caracterización tridimensional (arquitectura y volumen) de cultivos leñosos para el diseño de tratamientos de aplicación localizada de fitosanitarios dirigidos a la parte aérea de los mismos. Para afrontar el control localizado de herbicidas se han estudiado la configuración y las especificaciones técnicas de un UAV y de los sensores embarcados a bordo para su aplicación en la detección temprana de malas hierbas y contribuir a la generación de mapas para un control localizado en tres cultivos herbáceos: maíz, trigo y girasol. A continuación, se evaluaron los índices espectrales más precisos para su uso en la discriminación de suelo desnudo y vegetación (cultivo y malas hierbas) en imágenes-UAV tomadas sobre dichos cultivos en fase temprana. Con el fin de automatizar dicha discriminación se implementó en un entorno OBIA un método de cálculo de umbrales. Finalmente, se desarrolló una metodología OBIA automática y robusta para la discriminación de cultivo, suelo desnudo y malas hierbas en los tres cultivos estudiados, y se evaluó la influencia sobre su funcionamiento de distintos parámetros relacionados con la toma de imágenes UAV (solape, tipo de sensor, altitud de vuelo, momento de programación de los vuelos, entre otros). Por otra parte y para facilitar el diseño de tratamientos fitosanitarios ajustados a las necesidades de los cultivos leñosos se ha desarrollado una metodología OBIA automática y robusta para la caracterización tridimensional (arquitectura y volumen) de cultivos leñosos usando imágenes y modelos digitales de superficies generados a partir de imágenes procedentes de un UAV. Asimismo, se evaluó la influencia de distintos parámetros relacionados con la toma de las imágenes (solape, tipo de sensor, altitud de vuelo) sobre el funcionamiento del algoritmo OBIA diseñado