@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}
}
