@conference {8545397, title = {Convolutional Networks for Semantic Heads Segmentation using Top-View Depth Data in Crowded Environment}, booktitle = {2018 24th International Conference on Pattern Recognition (ICPR)}, year = {2018}, month = {Aug}, pages = {1384-1389}, abstract = {Detecting and tracking people is a challenging task in a persistent crowded environment (i.e. retail, airport, station, etc.) for human behaviour analysis of security purposes. This paper introduces an approach to track and detect people in cases of heavy occlusions based on CNNs for semantic segmentation using top-view depth visual data. The purpose is the design of a novel U-Net architecture, U-Net3, that has been modified compared to the previous ones at the end of each layer. In particular, a batch normalization is added after the first ReLU activation function and after each max-pooling and up-sampling functions. The approach was applied and tested on a new and public available dataset, TVHeads Dataset, consisting of depth images of people recorded from an RGB-D camera installed in top-view configuration. Our variant outperforms baseline architectures while remaining computationally efficient at inference time. Results show high accuracy, demonstrating the effectiveness and suitability of our approach.}, keywords = {Cameras, Computer architecture, Fractals, Head, Image segmentation, Semantics, Training}, issn = {1051-4651}, doi = {10.1109/ICPR.2018.8545397}, author = {Daniele Liciotti and Marina Paolanti and R. Pietrini and Emanuele Frontoni and Primo Zingaretti} } @article {Paolanti2018, title = {Person Re-Identification with RGB-D Camera in Top-View Configuration through Multiple Nearest Neighbor Classifiers and Neighborhood Component Features Selection}, journal = {Sensors}, volume = {18}, number = {10}, year = {2018}, month = {oct}, pages = {3471}, publisher = {{MDPI} {AG}}, doi = {10.3390/s18103471}, url = {https://doi.org/10.3390/s18103471}, author = {Marina Paolanti and Luca Romeo and Daniele Liciotti and Annalisa Cenci and Emanuele Frontoni and Primo Zingaretti} } @conference {liciotti2017hmm, title = {HMM-based activity recognition with a ceiling RGB-D camera}, booktitle = {ICPRAM (International Conference on Pattern Recognition Applications and Methods)}, year = {2017}, month = {02/2017}, abstract = {

Automated recognition of Activities of Daily Living allows to identify possible health problems and apply corrective strategies in Ambient Assisted Living (AAL). Activities of Daily Living analysis can provide very useful information for elder care and long-term care services. This paper presents an automated RGB-D video analysis system that recognises human ADLs activities, related to classical daily actions. The main goal is to predict the probability of an analysed subject action. Thus, the abnormal behaviour can be detected. The activity detection and recognition is performed using an affordable RGB-D camera. Human activities, despite their unstructured nature, tend to have a natural hierarchical structure; for instance, generally making a coffee involves a three-step process of turning on the coffee machine, putting sugar in cup and opening the fridge for milk. Action sequence recognition is then handled using a discriminative Hidden Markov Model (HMM). RADiaL, a dataset with RGB-D images and 3D position of each person for training as well as evaluating the HMM, has been built and made publicly available.

}, author = {Daniele Liciotti and Emanuele Frontoni and Primo Zingaretti and Bellotto, Nicola and Duckett, Tom} } @inbook {Liciotti2017, title = {An Intelligent RGB-D Video System for Bus Passenger Counting}, booktitle = {Intelligent Autonomous Systems 14: Proceedings of the 14th International Conference IAS-14}, year = {2017}, pages = {473{\textendash}484}, publisher = {Springer International Publishing}, organization = {Springer International Publishing}, address = {Cham}, isbn = {978-3-319-48036-7}, doi = {10.1007/978-3-319-48036-7_34}, url = {http://dx.doi.org/10.1007/978-3-319-48036-7_34}, author = {Daniele Liciotti and Annalisa Cenci and Emanuele Frontoni and Adriano Mancini and Primo Zingaretti}, editor = {Chen, Weidong and Hosoda, Koh and Menegatti, Emanuele and Shimizu, Masahiro and Wang, Hesheng} } @article {paolanti2017modelling, title = {Modelling and Forecasting Customer Navigation in Intelligent Retail Environments}, journal = {Journal of Intelligent \& Robotic Systems}, year = {2017}, month = {10}, pages = {1{\textendash}16}, abstract = {

Understanding shopper behaviour is one of the keys to success for retailers. In particular, it is necessary that managers know which retail attributes are important to which shoppers and their main goal is to improve the consumer shopping experience. In this work, we present sCREEN (Consumer REtail ExperieNce), an intelligent mechatronic system for indoor navigation assistance in retail environments that minimizes the need for active tagging and does not require metrics maps. The tracking system is based on Ultra-wideband technology. The digital devices are installed in the shopping carts and baskets and sCREEN allows modelling and forecasting customer navigation in retail environments. This paper contributes the design of an intelligent mechatronic system with the use of a novel Hidden Markov Models (HMMs) for the representation of shoppers{\textquoteright} shelf/category attraction and usual retail scenarios such as product out of stock or changes on store layout. Observations are viewed as a perceived intelligent system performance. By forecasting consumers next shelf/category attraction, the system can present the item location information to the consumer, including a walking route map to a location of the product in the retail store, and/or the number of an aisle in which the product is located. Effective and efficient design processes for mechatronic systems are a prerequisite for competitiveness in an intelligent retail environment. Experiments are performed in a real retail environment that is a German supermarket, during business hours. A dataset, with consumers trajectories, timestamps and the corresponding ground truth for training as well as evaluating the HMM, have been built and made publicly available. The results in terms of Precision, Recall and F1-score demonstrate the effectiveness and suitability of our approach, with a precision value that exceeds the 76\% in all test cases.

}, issn = {0921-0296}, doi = {10.1007/s10846-017-0674-7}, url = {https://doi.org/10.1007/s10846-017-0674-7}, author = {Marina Paolanti and Daniele Liciotti and R. Pietrini and Adriano Mancini and Emanuele Frontoni} } @conference {Cenci:2017:MAP:3109761.3109773, title = {Movements Analysis of Preterm Infants by Using Depth Sensor}, booktitle = {Proceedings of the 1st International Conference on Internet of Things and Machine Learning}, series = {IML {\textquoteright}17}, year = {2017}, month = {10-2017}, pages = {12:1{\textendash}12:9}, publisher = {ACM}, organization = {ACM}, address = {New York, NY, USA}, abstract = {

Qualitative assessment of general movements in preterm infants is widely used in clinical practice. It can enable early detection of neurological dysfunctions and consequent neuromotor impairments in high risk infants. However, the outcome of these assessments is not standardized and it is influenced by examiner{\textquoteright}s subjective interpretation. For this reason, there is an increasing interest in the use of automated movement recognition technologies being applied in this field. In this work, we use a video-based system for preterm infant{\textquoteright}s movements assessment to provide a 3D\ motion analysis method able to extract some important indicators from the sequence of depth images collected by using an RGB-D sensor placed over the infant lying on the crib. The advantage of the proposed method is that it is objective, contactless, non-invasive, easy to install, affordable and suitable to be used in an indoor environment with poor lighting, as might be rooms in the Neonatal Intensive Care Unit, where these infants are taken into care. Experimental results show that the proposed method is able to derive from statistical analysis of depth data some key performance indicators, each of which describes different characteristics of the infant{\textquoteright}s spontaneous movements. Preliminary tests are conducted in the experimental phase on a preterm infant hospitalized in a women{\textquoteright}s and children{\textquoteright}s hospital. The project can be used to investigate the relationship between the characteristics of spontaneous movements and the presence of pathologies as cerebral palsy or other minor neurological dysfunctions.

}, keywords = {3D tracking, clustering, modelling, preterm infant{\textquoteright}s movement analysis}, isbn = {978-1-4503-5243-7}, doi = {10.1145/3109761.3109773}, url = {http://doi.acm.org/10.1145/3109761.3109773}, author = {Annalisa Cenci and Daniele Liciotti and Emanuele Frontoni and Primo Zingaretti and Virgilio Paolo Carnielli} } @inbook {liciotti2017person, title = {Person Re-identification Dataset with RGB-D Camera in a Top-View Configuration}, booktitle = {Video Analytics. Face and Facial Expression Recognition and Audience Measurement}, volume = {1}, year = {2017}, author = {Daniele Liciotti and Marina Paolanti and Emanuele Frontoni and Adriano Mancini and Primo Zingaretti} } @inbook {liciotti2017pervasive, title = {Pervasive System for Consumer Behaviour Analysis in Retail Environments}, booktitle = {Video Analytics. Face and Facial Expression Recognition and Audience Measurement}, volume = {2}, year = {2017}, author = {Daniele Liciotti and Emanuele Frontoni and Adriano Mancini and Primo Zingaretti} } @conference {cenci2016cloud, title = {A cloud-based healthcare infrastructure for medical device integration: The bilirubinometer case study}, booktitle = {12th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications (MESA)}, year = {2016}, pages = {1{\textendash}6}, publisher = {IEEE}, organization = {IEEE}, author = {Annalisa Cenci and Daniele Liciotti and Ercoli, Ilaria and Primo Zingaretti and Virgilio Paolo Carnielli} } @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 {liciotti2015human, title = {Human activity analysis for in-home fall risk assessment}, booktitle = {Communication Workshop (ICCW), 2015 IEEE International Conference on}, year = {2015}, pages = {284{\textendash}289}, publisher = {IEEE}, organization = {IEEE}, author = {Daniele Liciotti and Gionata Massi and Emanuele Frontoni and Adriano Mancini 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 {cenci2015non, title = {Non-Contact Monitoring of Preterm Infants Using RGB-D Camera}, booktitle = {ASME 2015 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference}, year = {2015}, pages = {V009T07A003{\textendash}V009T07A003}, publisher = {American Society of Mechanical Engineers}, organization = {American Society of Mechanical Engineers}, author = {Annalisa Cenci and Daniele Liciotti and Emanuele Frontoni and Adriano Mancini and Primo Zingaretti} } @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} } @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} }