@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}
}
