@conference {,
	title = {An agent-based WCET analysis for Top-View Person Re-Identification},
	booktitle = {1st International Workshop on Real-Time Compliant Multi-Agent Systems (RTcMAS)},
	year = {2018},
	publisher = {CEUR Workshop Proceedings},
	organization = {CEUR Workshop Proceedings},
	address = {Stockholm},
	abstract = {<p>Person re-identification is a challenging task for improving and personalising the shopping experience in an intelligent retail environment. A new Top View Person Re-Identification (TVPR) dataset of 100 persons has been collected and described in a previous work. This work estimates the Worst Case Execution Time (WCET) for the features extraction and classification steps. Such tasks should not exceed the WCET, in order to ensure the effectiveness of the proposed application. In fact, after the features extraction, the classification process is performed by selecting the first passage under the camera for training and using the others as the testing set. Furthermore, a gender classification is exploited for improving retail applications. We tested all feature sets using k-Nearest Neighbors, Support Vector Machine, Decision Tree and Random Forest classifiers. Experimental results prove the effectiveness of the proposed approach, achieving good performance in terms of Precision, Recall and F1-score.</p>
},
	keywords = {Person re-identification, Real-time, Retail, RGB-D camera, WCET},
	url = {http://ceur-ws.org/Vol-2156/paper4.pdf},
	author = {Marina Paolanti and Valerio Placidi and Michele Bernardini and Andrea Felicetti and Rocco Pietrini and Emanuele Frontoni}
}
@conference {Liciotti2014,
	title = {An automatic analysis of shoppers behaviour using a distributed RGB-D cameras system},
	booktitle = {MESA 2014 - 10th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications, Conference Proceedings},
	year = {2014},
	note = {cited By 0},
	abstract = {The aim of this work is to propose an integrated system consisted of an RGB-D camera and software able to monitor shoppers in intelligent retail environments. We want to propose an innovative low cost intelligent system that can evaluate not only the shopper behaviour, but also detect their interactions with the products in the shelves, by developing automatic RGB-D techniques for video analysis. The system of cameras, located in strategic locations within the store, detects the presence of a person by identifying the blob and the centre of mass. The camera detects the person as an object in moving. Through the video frames, the system detects the interactions of the shoppers with the products on the shelf and establishes also the type of interaction: if a product is picked up, if the product is taken and then repositioned and finally, if there is not contact with the products. To understand the shopper behaviour is very important for the marketing strategies of a retail store. The proposed architecture monitors this aspect, that is low cost, easy to install and able to ensure very satisfactory results also in real environments.},
	doi = {10.1109/MESA.2014.6935617},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84911973081\&partnerID=40\&md5=57f2b74e422e1e28066bf350b02a97e2},
	author = {Daniele Liciotti and Primo Zingaretti and Valerio Placidi}
}
@article {Frontoni2014255,
	title = {Information management for intelligent retail environment: The shelf detector system},
	journal = {Information (Switzerland)},
	volume = {5},
	number = {2},
	year = {2014},
	note = {cited By 3},
	pages = {255-271},
	abstract = {Shelf-out-of-stock is one of the leading motivations of technology innovation in the shelf of the future. The Shelf Detector project described in this paper aims to solve the problem of data knowledge in the shelf-out-of-stock problem. This paper is mainly focused on the information layer of the system and main novelties illustrated in this work are in the information field demonstrating the huge number of insights that can be derived from the use of such a tool able to gather data in real time from the store. The tool presented is the first being installed for a long time in a high number of stores and products, demonstrating the ability to gather data and extract interesting insights. This paper aims to demonstrate the feasibility and the scalability of our system in providing a high number of data and interesting insights for store and marketing teams. The cloud based architecture developed and tested in this project is a key feature of our system together with the ability to collect data from a distributed sensor network. {\textcopyright} 2014 by the authors.},
	doi = {10.3390/info5020255},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84902662564\&partnerID=40\&md5=53f917be9988ce992278dc033771abf7},
	author = {Emanuele Frontoni and Adriano Mancini and Primo Zingaretti and Valerio Placidi}
}
@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}
}
@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 {Mancini2013,
	title = {Smart vision system for shelf analysis in intelligent retail environments},
	booktitle = {Proceedings of the ASME Design Engineering Technical Conference},
	volume = {4},
	year = {2013},
	note = {cited By 6},
	abstract = {This paper aims to propose an innovative idea of an embedded intelligent, multimedia and interactive shop system where embedded vision systems can analyse human behaviours around shelves for interactivity and statistical purposes, mostly devoted to customer behaviour analysis, planogram maintenance and out of stock detection. We discuss 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. In particular we focus on concepts of monitoring and interactivity, introducing several emerging technologies in the field of retail environments. The main novelty of the paper is the general architecture of the system together with the introduction of a series of intelligent embedded systems, yet implemented and tested in a dataset recorded during the Euro Shop trade fair, in cooperation with Grottini group, a leading company in Retail Design industry based in Italy. Results are convincing and most of all the general architecture is affordable in this specific application. Copyright {\textcopyright} 2013 by ASME.},
	doi = {10.1115/DETC2013-12317},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84899129851\&partnerID=40\&md5=9468fe9c8ec144cde0a360886f9aae83},
	author = {Adriano Mancini and Emanuele Frontoni and Primo Zingaretti and Valerio Placidi}
}
