Traditional museums, virtual museums. Dissemination role of ICTs.

Molti spazi della cultura, che si configurano come musei di sé stessi, presentano al loro interno pochi reperti esposti. È il caso di musei in edifici o aree archeologiche di seconda fascia, dai quali la maggior parte dei reperti è stata spostata in musei di importanza superiore o dove i reperti sono stati rimossi per diverse esigenze organizzative/espositive. In queste situazioni le ICT permettono di sviluppare un efficace sistema di comunicazione e disseminazione, coinvolgendo i visitatori e gli studiosi mediante l’utilizzo di procedure collegate all’Edutainment, all’interactive ed immersive experience, ai serious games e alla gamification. Come caso studio sono presi il Museo delle Mura, come museo in un edificio, e la Villa di Massenzio, come area archeologica, entrambi collocati sulla Via Appia Antica a Roma. Le esigenze della Sovrintendenza sono di valorizzare e divulgare: - la presenza del Museo, collocato in una delle numerose porte romane ancora ben conservate e site nel giro delle Mura Aureliane; - la storia della porta e del breve tratto di mura ad essa connesse; - la storia e l’articolazione delle mura di Roma. Per la Villa di Massenzio l’obiettivo principale è far comprendere la storia e la funzione delle due strutture (il circo ed il Mausoleo di Romolo), oggi visibili e visitabili, garantendo una maggiore comprensione di un’area di circa 4 ettari, in cui i visitatori oggi possono beneficiare solo di alcuni pannelli informativi.Many cultural spaces, which have been transformed into museums contain very few exhibits. In particular, museums in buildings or second-tier archaeological areas, where most of the finds have been moved to museums of major importance or exhibits that have been removed for different organizational/exhibition needs. In these situations, the use of ICT affords the possibility to incorporate effective communication and dissemination systems. As a result, it involves visitors and scholars within the exhibit using procedures related to edutainment, interactive and immersive experiences, serious games and gamification. As a case study are taken the Museum of the Walls, as a museum in building, and the archaeological area of the Maxentius archaeological complex, as an open-air museum, both located on the Ancient Appia road. In the Museum of the Walls Superintendent's requirements are to enhance and disseminate: - the presence of the Museum, located in one of the many well-preserved Roman city gates located in the Aurelian Walls; - the history of the city gate and of the short section of walls connected to it; - the history and articulation of the walls of Rome. In the Maxentius archaeological the main goal is to make understand the history and the function of the two main structures (the circus and a Mausoleum of Romulus), which are visible and open to visitors, ensuring a greater understanding of an area with the size of about 4 hectares, where visitors today can only benefit information from some panels

Large-Scale Mapping of Human Activity using Geo-Tagged Videos

This paper is the first work to perform spatio-temporal mapping of human activity using the visual content of geo-tagged videos. We utilize a recent deep-learning based video analysis framework, termed hidden two-stream networks, to recognize a range of activities in YouTube videos. This framework is efficient and can run in real time or faster which is important for recognizing events as they occur in streaming video or for reducing latency in analyzing already captured video. This is, in turn, important for using video in smart-city applications. We perform a series of experiments to show our approach is able to accurately map activities both spatially and temporally. We also demonstrate the advantages of using the visual content over the tags/titles.Comment: Accepted at ACM SIGSPATIAL 201

Driven to Distraction: Self-Supervised Distractor Learning for Robust Monocular Visual Odometry in Urban Environments

We present a self-supervised approach to ignoring "distractors" in camera images for the purposes of robustly estimating vehicle motion in cluttered urban environments. We leverage offline multi-session mapping approaches to automatically generate a per-pixel ephemerality mask and depth map for each input image, which we use to train a deep convolutional network. At run-time we use the predicted ephemerality and depth as an input to a monocular visual odometry (VO) pipeline, using either sparse features or dense photometric matching. Our approach yields metric-scale VO using only a single camera and can recover the correct egomotion even when 90% of the image is obscured by dynamic, independently moving objects. We evaluate our robust VO methods on more than 400km of driving from the Oxford RobotCar Dataset and demonstrate reduced odometry drift and significantly improved egomotion estimation in the presence of large moving vehicles in urban traffic.Comment: International Conference on Robotics and Automation (ICRA), 2018. Video summary: http://youtu.be/ebIrBn_nc-

Dynamic Body VSLAM with Semantic Constraints

Image based reconstruction of urban environments is a challenging problem that deals with optimization of large number of variables, and has several sources of errors like the presence of dynamic objects. Since most large scale approaches make the assumption of observing static scenes, dynamic objects are relegated to the noise modeling section of such systems. This is an approach of convenience since the RANSAC based framework used to compute most multiview geometric quantities for static scenes naturally confine dynamic objects to the class of outlier measurements. However, reconstructing dynamic objects along with the static environment helps us get a complete picture of an urban environment. Such understanding can then be used for important robotic tasks like path planning for autonomous navigation, obstacle tracking and avoidance, and other areas. In this paper, we propose a system for robust SLAM that works in both static and dynamic environments. To overcome the challenge of dynamic objects in the scene, we propose a new model to incorporate semantic constraints into the reconstruction algorithm. While some of these constraints are based on multi-layered dense CRFs trained over appearance as well as motion cues, other proposed constraints can be expressed as additional terms in the bundle adjustment optimization process that does iterative refinement of 3D structure and camera / object motion trajectories. We show results on the challenging KITTI urban dataset for accuracy of motion segmentation and reconstruction of the trajectory and shape of moving objects relative to ground truth. We are able to show average relative error reduction by a significant amount for moving object trajectory reconstruction relative to state-of-the-art methods like VISO 2, as well as standard bundle adjustment algorithms