Silvio Barra was born in Battipaglia (SA - ITALY) on August 7, 1985.
In 2009 and in 2012 he received the B.Sc. degree (cum laude) and the M.Sc. degree (cum laude) in Computer Science from University of Salerno. In 2017 he took the Ph.D. at the University of Cagliari. Currently he is Assistant Professor at the Department of Information Technology and Electrical Engineering at University of Naples, Federico II.
He is member of the GIRPR/CVPL.
His main research interests include pattern recognition, biometrics, video analysis and analytics and financial forecasting.
He is author in more than 40 papers, published in International Journals, Conferences and books.
Ph.D. Degree in Computer Science, 2016
University of Cagliari
Visiting Researcher, 2014
Universidade da Beira Interior, UBI (Covilhã, Portugal)
Master Degree (cum laude) in Computer Science, 2012
University of Salerno
Bachelor Degree (cum laude) in Computer Science, 2009
University of Salerno
March 31, 2021: New Updates to CV&DL for COVID project.
March 05, 2021: The paper entitled “Visual Question Answering: which investigated applications?” has been submitted to Pattern Recognition Letters.
March 04, 2021: Accepted Invitation to serve as International Program Committee Member for SIBGRAPI 2021 - Conference on Graphics, Patterns and Images
March 02, 2021: Accepted Invitation to serve as Program Committee Member Applications for Video Action Recognition and Prediction Special Session (AVARP2020), Special Session @ International Symposium on Intelligent Distributed Computing (IDC2020)
February 27, 2021: The paper entitled Natural interaction with traffic control cameras through multimodal interfaces has been submittedat AI-HCI 2021 as invited paper. The conference will be held virtually on 24-29 July 2021.
Febraury 24, 2021: The paper entitled FootApp: an AI-Powered System for Football Match Annotation has been submitted to the Special Issue Pattern Recognition for Adaptive User Interface on Multimedia Tools and Applications.
January 11, 2021: The paper entitled Heimdall: an AI-based infrastructure for traffic monitoring and anomalies detection has been accepted to PerAwareCity2021!The presentation will be on March 22, starting from 10:45 a.m. (CET). Alessandro Sebastian Podda is the designed presenter. The paper is developed within the SAFESPOTTER project.
Program Chair @ International Conferences and Workshops
Session Chair @ International Conferences and Workshops
Poster and Demo Chair @ International Conferences and Workshops
Program Committee Member @ International Conferences and Workshops
Mentorship Program @International Conferences and Workshops
The research project aims at financing researches and developments in the color rectal cancer diagnosis. In particular, the objecive is to enhance the knowledge in the field by proposing AI based techniques for colon-rectal cancer
MISTER is a project that aims to innovate and automate the mechanisms of Match Analysis in football. The basic idea is to improve video analysis and integrate it with that derived from other sources such as wearable devices or expert information.
This research project consists of a set of activities finalized to the classification of the prognosis of a patient affected by coronavirus. The project is in collaboration with Prof Salvatore Carta and Dr Sebastian Podda of the University of Cagliari.
The ever-growing number of travelers and migrants crossing the EU borders poses a serious challenge to the border control authorities in terms of a reduced amount of time for carrying out border checks.
Car Accident Prevention Systems The project aims at proposing a videosurveillance systems with the following objectives: developing a Smart Cities project for the pedestrian and drivers safety, for monitoring the terrtory, thus enhancing the road safety; reducing the intervention times (fire fighters, police and so on); analysing urban video and data in order to prevent accidents; modelling dangerous styles of driving; eventually identifying secondary paths for circumventing traffics and accidents, so to lighten urban traffic jams.
Head pose estimation is a sensitive topic in video surveillance/smart ambient scenarios since head rotations can hide/distort discriminative features of the face. Face recognition would often tackle the problem of video frames where subjects appear in poses making it quite impossible. In this respect, the selection of the frames with the best face orientation can allow triggering recognition only on these, therefore decreasing the possibility of errors. This paper proposes a novel approach to head pose estimation for smart cities and video surveillance scenarios, aiming at this goal. The method relies on a cascade of two models: the first one predicts the positions of 68 well-known face landmarks; the second one applies a web-shaped model over the detected landmarks, to associate each of them to a specific face sector. The method can work on detected faces at a reasonable distance and with a resolution that is supported by several present devices. Results of experiments executed over some classical pose estimation benchmarks, namely Point ‘04, Biwi, and AFLW datasets show good performance in terms of both pose estimation and computing time. Further results refer to noisy images that are typical of the addressed settings. Finally, examples demonstrate the selection of the best frames from videos captured in video surveillance conditions.
In the last decade, market financial forecasting has attracted high interests amongst the researchers in pattern recognition. Usually, the data used for analysing the market, and then gamble on its future trend, are provided as time series; this aspect, along with the high fluctuation of this kind of data, cuts out the use of very efficient classification tools, very popular in the state of the art, like the well known convolutional neural networks ( CNNs ) models such as Inception, ResNet, AlexNet, and so on. This forces the researchers to train new tools from scratch. Such operations could be very time consuming. This paper exploits an ensemble of CNNs, trained over Gramian angular fields ( GAF ) images, generated from time series related to the Standard - Poor’s 500 index future; the aim is the prediction of the future trend of the U.S. market. A multi-resolution imaging approach is used to feed each CNN, enabling the analysis of different time intervals for a single observation. A simple trading system based on the ensemble forecaster is used to evaluate the quality of the proposed approach. Our method outperforms the buy-And-hold ( B - H ) strategy in a time frame where the latter provides excellent returns. Both quantitative and qualitative results are provided.
Interest in the security of individuals has increased in recent years. This increase has in turn led to much wider deployment of surveillance cameras worldwide, and consequently, automated surveillance systems research has received more attention from the scientific community than before. Concurrently, biometrics research has become more popular as well, and it is supported by the increasing number of approaches devised to address specific degradation factors of unconstrained environments. Despite these recent efforts, no automated surveillance system that performs reliable biometric recognition in such an environment has become available. Nevertheless, recent developments in human motion analysis and biometric recognition suggest that both can be combined to develop a fully automated system. As such, this paper reviews recent advances in both areas, with a special focus on surveillance scenarios. When compared to previous studies, we highlight two distinct features, i.e., (1) our emphasis is on approaches that are devised to work in unconstrained environments and surveillance scenarios; and (2) biometric recognition is the final goal of the surveillance system, as opposed to behavior analysis, anomaly detection or action recognition.
Soft biometrics have been emerging to complement other traits and are particularly useful for poor quality data. In this paper, we propose an efficient algorithm to estimate human head poses and to infer soft biometric labels based on the 3D morphology of the human head. Starting by considering a set of pose hypotheses, we use a learning set of head shapes synthesized from anthropometric surveys to derive a set of 3D head centroids that constitutes a metric space. Next, representing queries by sets of 2D head landmarks, we use projective geometry techniques to rank efficiently the joint 3D head centroids/pose hypotheses according to their likelihood of matching each query. The rationale is that the most likely hypotheses are sufficiently close to the query, so a good solution can be found by convex energy minimization techniques. Once a solution has been found, the 3D head centroid and the query are assumed to have similar morphology, yielding the soft label. Our experiments point toward the usefulness of the proposed solution, which can improve the effectiveness of face recognizers and can also be used as a privacy-preserving solution for biometric recognition in public environments.
University of Naples, “Federico II”
University of Cagliari
University of Salerno
University of Naples, “Parthenope”
University of Rome, “Sapienza”
Universidade da Beira Interior (UBI) - Covilhã, Portugal
Universidad de Las Palmas de Gran Canaria (ULPGC)
Leonardo Piano, M.Sc. Student @ University of Cagliari, under the supervision of Prof. Salvatore Carta
Nicola Sansoni, B.Sc. Student @ University of Cagliari, under the supervision of Prof. Salvatore Carta
Andrea Atzori, M.Sc. Student @ University of Cagliari, under the supervision of Prof. Salvatore Carta
Marco Grazioso, Ph.D. Student @ University of Naples “Federico II”, under the supervision of Prof. Francesco Cutugno
Sondos Mohamed, Ph.D. Student @ University of Cagliari, under the supervision of Prof. Salvatore Carta
Mattia Carta, B.Sc. Student @ University of Cagliari, under the supervision of Prof. Salvatore Carta
Luigi Podda, B.Sc. Student @ University of Cagliari, under the supervision of Prof. Salvatore Carta
Andrea Deidda, B.Sc. Student @ University of Cagliari, under the supervision of Prof. Salvatore Carta
Alberto Musa, M.Sc. Student @ University of Cagliari, under the supervision of Prof. Salvatore Carta
Omar Filali, B.Sc. Student @ University of Cagliari, under the supervision of Prof. Salvatore Carta