@conference {Liciotti2014,
	title = {Advanced integration of multimedia assistive technologies: A prospective outlook},
	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 several studies on population ageing in the most advanced countries argued that the share of people older than 65 years is steadily increasing. In order to tackle this phenomena, a significant effort has been devoted to the development of advanced technologies for supervising the domestic environments and their inhabitants to provide them assistance in their own home. In this context, the present paper aims to delineate a novel, highly-integrated system for advanced analysis of human behaviours. It is based on the fusion of the audio and vision frameworks, developed at the Multimedia Assistive Technology Laboratory (MATeLab) of the Universit{\`a} Politecnica delle Marche, in order to operate in the ambient assisted living context exploiting audio-visual domain features. The existing video framework exploits vertical RGB-D sensors for people tracking, interaction analysis and users activities detection in domestic scenarios. The depth information has been used to remove the affect of the appearance variation and to evaluate users activities inside the home and in front of the fixtures. In addition, group interactions are monitored and analysed. On the other side, the audio framework recognises voice commands by continuously monitoring the acoustic home environment. In addition, a hands-free communication to a relative or to a healthcare centre is automatically triggered when a distress call is detected. Echo and interference cancellation algorithms guarantee the high-quality communication and reliable speech recognition, respectively. The system we intend to delineate, thus, exploits multi-domain information, gathered from audio and video frameworks each, and stores them in a remote cloud for instant processing and analysis of the scene. Related actions are consequently performed.},
	doi = {10.1109/MESA.2014.6935629},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84911977133\&partnerID=40\&md5=3a8fad94ccf6268631dbf553e9360956},
	author = {Daniele Liciotti and Ferroni, G. and Emanuele Frontoni and Squartini, S. and Principi, E. and Bonfigli, R. and Primo Zingaretti and Francesco Piazza}
}
@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}
}
