Mirco Sturari

Advanced Interaction with Paintings by Augmented Reality and High Resolution Visualization: A Real Case Exhibition

In this paper, an interactive installation system for the enjoyment of the cultural heritage in a real case museum environment is presented. By using Augmented Reality technology, mobile application and High Resolution visualization we provide the users with a visual augmentation of their surroundings and a touch interaction technique to display digital contents for cultural heritage promotion, allowing museum visitors to interact with digital contents in an intuitive and exciting manner. The exhibition here presented is the result of previous research over the use of new technologies e.g. Augmented Reality for Cultural Heritage promotion. Descriptions of the hardware system component and software development details are presented, with particular focus over the application implementation. Furthermore, we outline a possible Multimedia AR Installation connected with a semantic network.

Integrating elevation data and multispectral high-resolution images for an improved hybrid Land Use/Land Cover mapping

ABSTRACT The combination of elevation data together with multispectral high-resolution images is a new methodology for obtaining land use/land cover classification. It represents a step forward for both the accuracy and automation of LULC applications and allows users to setup thematic assignments through rules based on feature attributes and human expert interpretation of land usage. The synergy between different types of information means that LiDAR can give new hints at both the segmentation and hybrid classification steps, leading to a joint use of multispectral, spatial and elevation data. The output is a thematic map characterized by a custom-designed legend that is able to discriminate between land cover classes with similar spectral characteristics (level 3 of the CLC legend). Experimental results from a hilly farmland area with some urban structures (Musone river basin, Ancona, Italy) are used to highlight how the proposed methodology enhances land cover classification in heterogeneous environments.

Mobile robot for retail surveying and inventory using visual and textual analysis of monocular pictures based on deep learning

This paper describes a novel system for automating data collection and surveying in a retail store using mobile robots. The manpower cost for surveying and monitoring the shelves in retail stores are high, because of which these activities are not repeated frequently causing reduced customer satisfaction and loss of revenue. Further, the accuracy of data collected may be improved by avoiding human related factors. We use a mobile robot platform with on-board cameras to monitor the shelves autonomously (based on indoor UWB Localization and planning). The robot is designed to facilitate automatic detection of Shelf Out of Stock (SOOS) situations. The paper contribution is an approach to estimate the overall stock assortment based of pictures from both visual and textual clues. Based on visual and textual features extracted from two trained Convolutional Neural Networks (CNNs), the type of the product is identified by a machine learning classifier. The approach was applied and tested on a newly collected dataset and several machine learning algorithms are compared. The experiments yield high accuracy, demonstrating the effectiveness and suitability of the proposed approach, also in comparison of existing state of the art SOOS solutions.

Robotic platform for deep change detection for rail safety and security

Felix is the first robot measuring switch dimension parameters and geometrical railway ones together with potentially dangerous situations. It increases the quality and the consistency of the measures and offers the chance to increase the safety of operators and ultimately of final users. This paper presents a novel approach mixing visual and point cloud information for effective change detection for railways safety and security application. Main contributions are on the proposed data platform and on the mix of point-based measurements (switch dimensions) and on surrounding change detection (dangerous trees or abnormal railway trawlers) based multi-view camera and linear laser, processed by a classification process. Results, coming from a real rail scenario using the Felix platform, show the feasibility of the approach and the fast surveying capabilities with strong implications on safety and security.

An integrated mobility system using real-time data for traffic simulation

Traffic lights are very often equipped with cameras for smart disengagement of crossings. Surveillance cameras are placed in many key-points of the citys mobility for security purposes, such as neighbourhood entrances and parking areas. The idea of this study is to use this in place infrastructure, already installed and maintained by mobility stakeholders, without additional hardware costs. Combining these mobile and fixed sensors we created a Wireless Sensor Network and built on it a Geographic Information System (GIS) platform to manage and monitor urban mobility. We extended this integrated system with an interface to Simulation of Urban MObility (SUMO), an open-source microscopic and continuous road traffic simulation package. Input files of simulator are generated by the system from real mobility data. In this way our system manages and monitors urban mobility in real-time providing an useful instrument for planning the interventions mobility plans/infrastructures, being able to simulate the future state and further verify the effectiveness of what is realized.