@conference {contigiani2016implementation,
	title = {Implementation of a tracking system based on UWB technology in a retail environment},
	booktitle = {Mechatronic and Embedded Systems and Applications (MESA), 2016 12th IEEE/ASME International Conference on},
	year = {2016},
	pages = {1{\textendash}6},
	publisher = {IEEE},
	organization = {IEEE},
	author = {Marco Contigiani and R. Pietrini and Adriano Mancini and Primo Zingaretti}
}
@article {Sturari2016,
	title = {Robust and affordable retail customer profiling by vision and radio beacon sensor fusion},
	journal = {Pattern Recognition Letters},
	year = {2016},
	pages = {-},
	abstract = {Abstract The development of reliable and precise indoor localization systems would considerably improve the ability to investigate shopper movements and behaviour inside retail environments. Previous approaches used either computer vision technologies or the analysis of signals emitted by communication devices (beacons). While computer vision approaches provide higher level of accuracy, beacons cover a wider operational area. In this paper, we propose a sensor fusion approach between active radio beacons and RGB-D cameras. This system, used in an intelligent retail environment where cameras are already installed for other purposes, allows an affordable environment set-up and a low operational costs for customer indoor localization and tracking. We adopted a Kalman filter to fuse localization data from radio signals emitted by beacons are used to track users{\textquoteright} mobile devices and RGB-D cameras used to refine position estimations. By combing coarse localization datasets from active beacons and RGB-D data from sparse cameras, we demonstrate that the indoor position estimation is strongly enhanced. The aim of this general framework is to provide retailers with useful information by analysing consumer activities inside the store. To prove the robustness of our approach, several tests were conducted into a real indoor showroom by analysing real customers behaviour with encouraging results.},
	issn = {0167-8655},
	doi = {http://dx.doi.org/10.1016/j.patrec.2016.02.010},
	url = {http://www.sciencedirect.com/science/article/pii/S016786551600057X},
	author = {Mirco Sturari and Daniele Liciotti and Roberto Pierdicca and Emanuele Frontoni and Adriano Mancini and Marco Contigiani and Primo Zingaretti}
}
@conference {pierdicca2015low,
	title = {Low cost embedded system for increasing retail environment intelligence},
	booktitle = {Multimedia \& Expo Workshops (ICMEW), 2015 IEEE International Conference on},
	year = {2015},
	pages = {1{\textendash}6},
	publisher = {IEEE},
	organization = {IEEE},
	author = {Roberto Pierdicca and Daniele Liciotti and Marco Contigiani and Emanuele Frontoni and Adriano Mancini and Primo Zingaretti}
}
@conference {Frontoni2014,
	title = {A heuristic approach to evaluate occurrences of products for the planogram maintenance},
	booktitle = {MESA 2014 - 10th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications, Conference Proceedings},
	year = {2014},
	note = {cited By 0},
	abstract = {Planogram is a detailed visual map of products in a retail store and establishes the position of products in order to increase sales and to supply the best location for suppliers. So, the aims of {\textquoteright}correct{\textquoteright} planogram are several and are: increasing sales, increasing profits, introducing a new item, supporting an innovative merchandising approach, and better manages the shelves. Deviating from the planogram defeats the purpose of any of those goals. A fundamental aspect in the retail operations is to maintain the integrity of the planogram. This work intends to provide a solution to this problem, proposing a system that individually identifies the presence of a specific product in the image of a shelf. This even though the product is moved, rotated, misplaced, or even in poor lighting conditions. This paper presents a method to find and count multiple instances of the same object that occurs into an image of a shelf in a store without using classifiers. The procedures here described are based on a heuristic algorithm that involves morphological operation, template matching and histogram comparison. Experimental results are presented in order to verify the effectiveness of the proposed approach. They demonstrate that the algorithm provides satisfactory results when the user manually chooses the most significant label of the product to find in the shelf image.},
	doi = {10.1109/MESA.2014.6935615},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84911965097\&partnerID=40\&md5=0de9992bbb2157ad432a05fee65d6dfc},
	author = {Emanuele Frontoni and Marco Contigiani and Ribighini, G.}
}
@conference {Contigiani2014,
	title = {Indoor people localization and tracking using an energy harvesting smart floor},
	booktitle = {MESA 2014 - 10th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications, Conference Proceedings},
	year = {2014},
	note = {cited By 0},
	abstract = {In the recent years, different kind of intelligent floors have been proposed but they are expensive and in many cases difficult to install and manage. In this paper, we previously describe the state of the art concerning smart floors designed to satisfy different applications and then we present an intelligent floor realized for localizing and tracking people in an indoor environment. The aim of this work is to propose an innovative smart floor based on an energy harvesting system able to allow the localization and analysis of the movement of the users in a specific space. The solution presented in this paper involves the use of capacitive sensors on a polymeric support to insert between solid wood and a wooden part of a floating parquet. The proposed system is part of HDOMO, an Ambient Assisted Living (AAL) project developing smart solutions for active aging. A detailed architecture of the smart floor is proposed together with a preliminary test phase.},
	doi = {10.1109/MESA.2014.6935623},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84911959515\&partnerID=40\&md5=e0736aa6e37701f6950aee45457ef457},
	author = {Marco Contigiani and Emanuele Frontoni and Adriano Mancini and A. Gatto}
}
@article {Liciotti2014,
	title = {Shopper analytics: A customer activity recognition system using a distributed rgb-d camera network},
	journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {8811},
	year = {2014},
	note = {cited By 0},
	abstract = {The aim of this paper is to present an integrated system consisted of a RGB-D camera and a software able to monitor shoppers in intelligent retail environments. We propose an innovative low cost smart system that can understand the shoppers{\textquoteright} behavior and, in particular, their interactions with the products in the shelves, with the aim to develop an automatic RGB-D technique for video analysis. The system of cameras detects the presence of people and univocally identifies them. Through the depth frames, the system detects the interactions of the shoppers with the products on the shelf and determines if a product is picked up or if the product is taken and then put back and finally, if there is not contact with the products. The system is low cost and easy to install, and experimental results demonstrated that its performances are satisfactory also in real environments.},
	doi = {10.1007/978-3-319-12811-5_11},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84921652873\&partnerID=40\&md5=44bc641a712fa9af6ffd341f41b0aec0},
	author = {Daniele Liciotti and Marco Contigiani and Emanuele Frontoni and Adriano Mancini and Primo Zingaretti and Valerio Placidi}
}
