@conference {8545397,
	title = {Convolutional Networks for Semantic Heads Segmentation using Top-View Depth Data in Crowded Environment},
	booktitle = {2018 24th International Conference on Pattern Recognition (ICPR)},
	year = {2018},
	month = {Aug},
	pages = {1384-1389},
	abstract = {Detecting and tracking people is a challenging task in a persistent crowded environment (i.e. retail, airport, station, etc.) for human behaviour analysis of security purposes. This paper introduces an approach to track and detect people in cases of heavy occlusions based on CNNs for semantic segmentation using top-view depth visual data. The purpose is the design of a novel U-Net architecture, U-Net3, that has been modified compared to the previous ones at the end of each layer. In particular, a batch normalization is added after the first ReLU activation function and after each max-pooling and up-sampling functions. The approach was applied and tested on a new and public available dataset, TVHeads Dataset, consisting of depth images of people recorded from an RGB-D camera installed in top-view configuration. Our variant outperforms baseline architectures while remaining computationally efficient at inference time. Results show high accuracy, demonstrating the effectiveness and suitability of our approach.},
	keywords = {Cameras, Computer architecture, Fractals, Head, Image segmentation, Semantics, Training},
	issn = {1051-4651},
	doi = {10.1109/ICPR.2018.8545397},
	author = {Daniele Liciotti and Marina Paolanti and R. Pietrini and Emanuele Frontoni and Primo Zingaretti}
}
@conference {pierdicca2016cyberarchaeology,
	title = {Cyberarchaeology: improved way findings for archaeological parks through mobile augmented reality},
	booktitle = {International Conference on Augmented Reality, Virtual Reality and Computer Graphics},
	year = {2016},
	pages = {172{\textendash}185},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	author = {Roberto Pierdicca and Emanuele Frontoni and Primo Zingaretti and Eva Savina Malinverni and Galli, Andrea and Marcheggiani, Ernesto and Costa, Carlos Smaniotto}
}
@article {Frontoni2013509,
	title = {Customers{\textquoteright} activity recognition in intelligent retail environments},
	journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {8158 LNCS},
	year = {2013},
	note = {cited By 6},
	pages = {509-516},
	abstract = {This paper aims to propose a novel idea of an embedded intelligent system where low cost embedded vision systems can analyze human behaviors to obtain interactivity and statistical data, mainly devoted to customer behavior analysis. In this project we addressed the need for new services into the shop, involving consumers more directly and instigating them to increase their satisfaction and, as a consequence, their purchases. To do this, technology is very important and allows making interactions between costumers and products and between customers and the environment of the shop a rich source of marketing analysis. We construct a novel system that uses vertical RGBD sensor for people counting and shelf interaction analysis, where the depth information is used to remove the affect of the appearance variation and to evaluate customers{\textquoteright} activities inside the store and in front of the shelf, with products. Also group interactions are monitored and analyzed with the main goal of having a better knowledge of the customers{\textquoteright} activities, using real data in real time. Even if preliminary, results are convincing and most of all the general architecture is affordable in this specific application, robust, easy to install and maintain and low cost. {\textcopyright} 2013 Springer-Verlag.},
	doi = {10.1007/978-3-642-41190-8_55},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84887121570\&partnerID=40\&md5=8a6e9b3442d8d4dbd47b404bd8248a7b},
	author = {Emanuele Frontoni and Raspa, P. and Adriano Mancini and Primo Zingaretti and Valerio Placidi}
}
@conference {Mancini2011745,
	title = {Coalition formation for unmanned quadrotors},
	booktitle = {Proceedings of the ASME Design Engineering Technical Conference},
	volume = {3},
	number = {PARTS A AND B},
	year = {2011},
	note = {cited By 1},
	pages = {745-752},
	abstract = {Today Unmanned Aerial Vehicles (UAVs) and in particular quad-rotors represent novel platforms to accomplish a wide set of missions as surveillance, Search \& Rescue, inspection, pho-togrammetry. The main limitation of these vehicles is represented by the restricted operating area. The area is mainly limited by power supplies (batteries or fuel). A strategy to overcame this limitation is to increase the number of vehicles forming a coalition. The main benefit of coalition formation are the extended mission range and the capability to increase the sensorial set. Each vehicles is a part of a dynamic network that must be properly coordinated in order to optimize all the available resources. In this paper a new framework for simulation of unmanned vehicles in cooperative scenarios is first presented. The framework is based on the interaction of a physics-engine, which simulates the dynamics of vehicles and their interaction with world increasing the realism of simulation, and a simulation environment where the high-level strategy is designed/developed. A Model Predictive Control (MPC) is then introduced to solve the problem of leader-follower applied to quad-rotors. Using the developed framework and the MPC technique is possible to easily instantiate the coalition minimizing also a cost function. The obtained results from the control strategy point of view show that positioning error at steady state is equal to zero. The MPC allows also the modelling of different conflicting constraints as the control actions, positioning error, and fuel/energy consumption. {\textcopyright} 2011 by ASME.},
	doi = {10.1115/DETC2011-48904},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84863584753\&partnerID=40\&md5=9d10818fcd0cd5219ab5df1c9eb26502},
	author = {Adriano Mancini and Benini, A. and Emanuele Frontoni and Primo Zingaretti and Sauro Longhi}
}
@conference {Zingaretti2007,
	title = {Comparison and fusion of vision and range measurements for robot pose estimation},
	booktitle = {2007 Mediterranean Conference on Control and Automation, MED},
	year = {2007},
	note = {cited By 0},
	abstract = {Multiple sensor fusion for robot pose estimation has attracted a lot of interest in recent years. Monte Carlo Localization (MCL) is a common method for self-localization of a mobile robot under the assumption that a map of the environment is available. In this paper we first compare pure vision-based with sonar-based MCL approaches in terms of localization accuracy, and then we show how the fusion of vision and range measurements improves the overall accuracy. Experiments were performed in an environment with high perceptual aliasing like our department corridors. They demonstrated that fusing simple and computationally inexpensive sensory information, coming from omnidirectional cameras and sonar sensors, can allow a mobile robot to precisely locate itself. {\textcopyright}2007 IEEE.},
	doi = {10.1109/MED.2007.4433854},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-50249102768\&partnerID=40\&md5=fbd623d1f43921d58130364b7f5c60ec},
	author = {Primo Zingaretti and Emanuele Frontoni}
}
