Egocentric Visitors Localization in Cultural Sites We consider the problem of localizing visitors in a cultural site from egocentric (first person) images. Localization information can be useful both to assist the user during his visit (e.g., by suggesting where to go and what to see next) and to provide behavioral information to the manager of the cultural site (e.g., how much time has been spent by visitors at a given location? What has been liked most?). To tackle the problem, we collected a large dataset of egocentric videos using two cameras: a headmounted HoloLens device and a chestmounted GoPro. Each frame has been labeled according to the location of the visitor and to what he was looking at. The dataset is freely available in order to encourage research in this domain. The dataset is complemented with baseline experiments performed considering a stateoftheart method for locationbased temporal segmentation of egocentric videos. Experiments show that compelling results can be achieved to extract useful information for both the visitor and the sitemanager.

Visual SLAM in Human Populated Environments: Exploring the Tradeoff between Accuracy and Speed of YOLO and Mask RCNN Simultaneous Localization and Mapping (SLAM) is a fundamental problem in mobile robotics. However, the majority of Visual SLAM algorithms assume a static scenario, limiting their applicability in realworld environments. Dealing with dynamic content in Visual SLAM is still an open problem, with solutions usually relying on direct or featurebased methods. Deep learning techniques can improve the SLAM solution in environments with a priori dynamic objects, providing highlevel information of the scene. This paper presents a new approach to SLAM in human populated environments using deep learningbased techniques. The system is built on ORBSLAM2, a stateoftheart SLAM system. The proposed methodology is evaluated using a benchmark dataset, outperforming other Visual SLAM methods in highly dynamic scenarios.