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2021
Conference article
Open Access
A multi-resolution training for expression recognition in the wild
Massoli F V, Cafarelli D, Amato G, Falchi F
Facial expressions play a fundamental role in human communication, and their study, which represents a multidisciplinary subject, embraces a great variety of research fields, e.g., from psychology to computer science, among others. Concerning Deep Learning, the recognition of facial expressions is a task named Facial Expression Recognition (FER). With such an objective, the goal of a learning model is to classify human emotions starting from a facial image of a given subject. Typically, face images are acquired by cameras that have, by nature, different characteristics, such as the output resolution. Moreover, other circumstances might involve cameras placed far from the observed scene, thus obtaining faces with very low resolutions. Therefore, since the FER task might involve analyzing face images that can be acquired with heterogeneous sources, it is plausible to expect that resolution plays a vital role. In such a context, we propose a multi-resolution training approach to solve the FER task. We ground our intuition on the observation that, often, face images are acquired at different resolutions. Thus, directly considering such property while training a model can help achieve higher performance on recognizing facial expressions. To our aim, we use a ResNet-like architecture, equipped with Squeeze-and-Excitation blocks, trained on the Affect-in-the-Wild 2 dataset. Not being available a test set, we conduct tests and model selection by employing the validation set only on which we achieve more than 90% accuracy on classifying the seven expressions that the dataset comprises.Source: CEUR WORKSHOP PROCEEDINGS, pp. 427-433. Pizzo Calabro, 5-9/9/2021
Project(s): AI4EU
Massoli F V, Cafarelli D, Amato G, Falchi F
Facial expressions play a fundamental role in human communication, and their study, which represents a multidisciplinary subject, embraces a great variety of research fields, e.g., from psychology to computer science, among others. Concerning Deep Learning, the recognition of facial expressions is a task named Facial Expression Recognition (FER). With such an objective, the goal of a learning model is to classify human emotions starting from a facial image of a given subject. Typically, face images are acquired by cameras that have, by nature, different characteristics, such as the output resolution. Moreover, other circumstances might involve cameras placed far from the observed scene, thus obtaining faces with very low resolutions. Therefore, since the FER task might involve analyzing face images that can be acquired with heterogeneous sources, it is plausible to expect that resolution plays a vital role. In such a context, we propose a multi-resolution training approach to solve the FER task. We ground our intuition on the observation that, often, face images are acquired at different resolutions. Thus, directly considering such property while training a model can help achieve higher performance on recognizing facial expressions. To our aim, we use a ResNet-like architecture, equipped with Squeeze-and-Excitation blocks, trained on the Affect-in-the-Wild 2 dataset. Not being available a test set, we conduct tests and model selection by employing the validation set only on which we achieve more than 90% accuracy on classifying the seven expressions that the dataset comprises.Source: CEUR WORKSHOP PROCEEDINGS, pp. 427-433. Pizzo Calabro, 5-9/9/2021
Project(s): AI4EU
See at:
ceur-ws.org 
| CNR IRIS | ISTI Repository | CNR IRIS 
2021
Other
Open Access
NAUSICAA - D1.2: Prototipi Analisi Visuale
Vadicamo L, Gennaro C, Cafarelli D, Falchi F
In questo documento vengono descritte le principali attività svolte nell'ambito dell'Obiettivo Operativo n. 1 (OO1) "Progettazione dei sistemi di Intelligenza Artificiale e di Visione Artificiale per la sicurezza dell'imbarcazione" e in particolare dell'Attività A1.2 "Realizzazione prima versione prototipi Analisi Visuale".
Vadicamo L, Gennaro C, Cafarelli D, Falchi F
In questo documento vengono descritte le principali attività svolte nell'ambito dell'Obiettivo Operativo n. 1 (OO1) "Progettazione dei sistemi di Intelligenza Artificiale e di Visione Artificiale per la sicurezza dell'imbarcazione" e in particolare dell'Attività A1.2 "Realizzazione prima versione prototipi Analisi Visuale".
See at:
CNR IRIS | ISTI Repository | CNR IRIS
2022
Conference article
Open Access
MOBDrone: a drone video dataset for Man OverBoard Rescue
Cafarelli D, Ciampi L, Vadicamo L, Gennaro C, Berton A, Paterni M, Benvenuti C, Passera M, Falchi F
Modern Unmanned Aerial Vehicles (UAV) equipped with cameras can play an essential role in speeding up the identification and rescue of people who have fallen overboard, i.e., man overboard (MOB). To this end, Artificial Intelligence techniques can be leveraged for the automatic understanding of visual data acquired from drones. However, detecting people at sea in aerial imagery is challenging primarily due to the lack of specialized annotated datasets for training and testing detectors for this task. To fill this gap, we introduce and publicly release the MOBDrone benchmark, a collection of more than 125K drone-view images in a marine environment under several conditions, such as different altitudes, camera shooting angles, and illumination. We manually annotated more than 180K objects, of which about 113K man overboard, precisely localizing them with bounding boxes. Moreover, we conduct a thorough performance analysis of several state-of-the-art object detectors on the MOBDrone data, serving as baselines for further research.DOI: 10.1007/978-3-031-06430-2_53
Metrics:
Cafarelli D, Ciampi L, Vadicamo L, Gennaro C, Berton A, Paterni M, Benvenuti C, Passera M, Falchi F
Modern Unmanned Aerial Vehicles (UAV) equipped with cameras can play an essential role in speeding up the identification and rescue of people who have fallen overboard, i.e., man overboard (MOB). To this end, Artificial Intelligence techniques can be leveraged for the automatic understanding of visual data acquired from drones. However, detecting people at sea in aerial imagery is challenging primarily due to the lack of specialized annotated datasets for training and testing detectors for this task. To fill this gap, we introduce and publicly release the MOBDrone benchmark, a collection of more than 125K drone-view images in a marine environment under several conditions, such as different altitudes, camera shooting angles, and illumination. We manually annotated more than 180K objects, of which about 113K man overboard, precisely localizing them with bounding boxes. Moreover, we conduct a thorough performance analysis of several state-of-the-art object detectors on the MOBDrone data, serving as baselines for further research.DOI: 10.1007/978-3-031-06430-2_53
Metrics:
See at:
CNR IRIS | link.springer.com | ISTI Repository | CNR IRIS | CNR IRIS
2022
Dataset
Open Access
MOBDrone: a large-scale drone-view dataset for man overboard detection
Cafarelli D, Ciampi L, Vadicamo L, Gennaro C, Berton A, Paterni M, Benvenuti C, Passera M, Falchi F
The Man OverBoard Drone (MOBDrone) dataset is a large-scale collection of aerial footage images. It contains 126,170 frames extracted from 66 video clips gathered from one UAV flying at an altitude of 10 to 60 meters above the mean sea level. Images are manually annotated with more than 180K bounding boxes localizing objects belonging to 5 categories --- person, boat, lifebuoy, surfboard, wood. More than 113K of these bounding boxes belong to the person category and localize people in the water simulating the need to be rescued.
Cafarelli D, Ciampi L, Vadicamo L, Gennaro C, Berton A, Paterni M, Benvenuti C, Passera M, Falchi F
The Man OverBoard Drone (MOBDrone) dataset is a large-scale collection of aerial footage images. It contains 126,170 frames extracted from 66 video clips gathered from one UAV flying at an altitude of 10 to 60 meters above the mean sea level. Images are manually annotated with more than 180K bounding boxes localizing objects belonging to 5 categories --- person, boat, lifebuoy, surfboard, wood. More than 113K of these bounding boxes belong to the person category and localize people in the water simulating the need to be rescued.
See at:
CNR IRIS | ISTI Repository | zenodo.org | CNR IRIS
2022
Software
Open Access
MOBDrone App
Cafarelli D, Vairo C, Gennaro C, Vadicamo L, Falchi F
MOBdrone is an android app developed as part of the NAUSICAA project for automatically searching for people who have fallen overboard. It uses DJI's sdk for automatic drone flight and integrates with a DLL for interaction with the ship's dashboard and a python app for visual analysis of captured video.
Cafarelli D, Vairo C, Gennaro C, Vadicamo L, Falchi F
MOBdrone is an android app developed as part of the NAUSICAA project for automatically searching for people who have fallen overboard. It uses DJI's sdk for automatic drone flight and integrates with a DLL for interaction with the ship's dashboard and a python app for visual analysis of captured video.
See at:
gitea-s2i2s.isti.cnr.it | CNR IRIS | CNR IRIS
2022
Software
Metadata Only Access
NADLibrary
Cafarelli D, Vairo C, Gennaro C, Vadicamo L, Falchi F
NADLibrary is a DLL library developed as part of the NAUSICAA project, which acts as a communication interface between the Windows application running on the ship's dashboard for remote control of the drone and the android application that controls the drone.
Cafarelli D, Vairo C, Gennaro C, Vadicamo L, Falchi F
NADLibrary is a DLL library developed as part of the NAUSICAA project, which acts as a communication interface between the Windows application running on the ship's dashboard for remote control of the drone and the android application that controls the drone.
See at:
gitea-s2i2s.isti.cnr.it | CNR IRIS
2022
Software
Open Access
Dummy drone dashboard
Cafarelli D, Vairo C, Gennaro C, Vadicamo L, Falchi F
Dummy drone dashboard is a C# interface, developed as part of the NAUSICAA project, that emulates the ship's dashboard to test the communication DLL developed for communication between the ship's dashboard and the android application that controls the drone.
Cafarelli D, Vairo C, Gennaro C, Vadicamo L, Falchi F
Dummy drone dashboard is a C# interface, developed as part of the NAUSICAA project, that emulates the ship's dashboard to test the communication DLL developed for communication between the ship's dashboard and the android application that controls the drone.
See at:
gitea-s2i2s.isti.cnr.it | CNR IRIS | CNR IRIS
2022
Other
Open Access
NAUSICAA - D1.4: Prototipi analisi visuale
Vadicamo L, Vairo C, Ciampi L, Gennaro C, Cafarelli D, Falchi F
In questo documento vengono descritte le principali attività svolte nell'ambito dell'Obiettivo Operativo n. 1 (OO1) "Progettazione dei sistemi di Intelligenza Artificiale e di Visione Artificiale per la sicurezza dell'imbarcazione" e in particolare dell'attività A1.4 "Realizzazione seconda versione dei prototipi Analisi Visuale" Tale attività ha avuto per scopo la realizzazione della seconda versione del prototipo per il riconoscimento e il tracking automatico di persone in mare e oggetti all'interno di flussi video provenienti da fonti eterogenee.
Vadicamo L, Vairo C, Ciampi L, Gennaro C, Cafarelli D, Falchi F
In questo documento vengono descritte le principali attività svolte nell'ambito dell'Obiettivo Operativo n. 1 (OO1) "Progettazione dei sistemi di Intelligenza Artificiale e di Visione Artificiale per la sicurezza dell'imbarcazione" e in particolare dell'attività A1.4 "Realizzazione seconda versione dei prototipi Analisi Visuale" Tale attività ha avuto per scopo la realizzazione della seconda versione del prototipo per il riconoscimento e il tracking automatico di persone in mare e oggetti all'interno di flussi video provenienti da fonti eterogenee.
See at:
CNR IRIS | ISTI Repository | CNR IRIS
2022
Conference article
Open Access
AIMH Lab: Smart Cameras for Public Administration
Ciampi L, Cafarelli D, Carrara F, Di Benedetto M, Falchi F, Gennaro C, Massoli Fv, Messina N, Amato G
In this short paper, we report the activities of the Artificial Intelligence for Media and Humanities (AIMH) laboratory of the ISTI-CNR related to Public Administration. In particular, we present some AI-based public services serving the citizens that help achieve common goals beneficial to the society, putting humans at the epicenter. Through the automatic analysis of images gathered from city cameras, we provide AI applications ranging from smart parking and smart mobility to human activity monitoring.
Ciampi L, Cafarelli D, Carrara F, Di Benedetto M, Falchi F, Gennaro C, Massoli Fv, Messina N, Amato G
In this short paper, we report the activities of the Artificial Intelligence for Media and Humanities (AIMH) laboratory of the ISTI-CNR related to Public Administration. In particular, we present some AI-based public services serving the citizens that help achieve common goals beneficial to the society, putting humans at the epicenter. Through the automatic analysis of images gathered from city cameras, we provide AI applications ranging from smart parking and smart mobility to human activity monitoring.
See at:
CNR IRIS | ISTI Repository | www.ital-ia2022.it | CNR IRIS
2021
Other
Open Access
AIMH research activities 2021
Aloia N., Amato G., Bartalesi Lenzi V., Benedetti F., Bolettieri P., Cafarelli D., Carrara F., Casarosa V., Coccomini D., Ciampi L., Concordia C., Corbara S., Di Benedetto M., Esuli A., Falchi F., Gennaro C., Lagani G., Massoli F. V., Meghini C., Messina N., Metilli D., Molinari A., Moreo Fernandez A., Nardi A., Pedrotti A., Pratelli N., Rabitti F., Savino P., Sebastiani F., Sperduti G., Thanos C., Trupiano L., Vadicamo L., Vairo C.
The Artificial Intelligence for Media and Humanities laboratory (AIMH) has the mission to investigate and advance the state of the art in the Artificial Intelligence field, specifically addressing applications to digital media and digital humanities, and taking also into account issues related to scalability. This report summarize the 2021 activities of the research group.DOI: 10.32079/isti-ar-2021/003
Metrics:
Aloia N., Amato G., Bartalesi Lenzi V., Benedetti F., Bolettieri P., Cafarelli D., Carrara F., Casarosa V., Coccomini D., Ciampi L., Concordia C., Corbara S., Di Benedetto M., Esuli A., Falchi F., Gennaro C., Lagani G., Massoli F. V., Meghini C., Messina N., Metilli D., Molinari A., Moreo Fernandez A., Nardi A., Pedrotti A., Pratelli N., Rabitti F., Savino P., Sebastiani F., Sperduti G., Thanos C., Trupiano L., Vadicamo L., Vairo C.
The Artificial Intelligence for Media and Humanities laboratory (AIMH) has the mission to investigate and advance the state of the art in the Artificial Intelligence field, specifically addressing applications to digital media and digital humanities, and taking also into account issues related to scalability. This report summarize the 2021 activities of the research group.DOI: 10.32079/isti-ar-2021/003
Metrics:
See at:
CNR IRIS | ISTI Repository | CNR IRIS
2022
Other
Open Access
AIMH research activities 2022
Aloia N., Amato G., Bartalesi Lenzi V., Benedetti F., Bolettieri P., Cafarelli D., Carrara F., Casarosa V., Ciampi L., Coccomini D. A., Concordia C., Corbara S., Di Benedetto M., Esuli A., Falchi F., Gennaro C., Lagani G., Lenzi E., Meghini C., Messina N., Metilli D., Molinari A., Moreo Fernandez A. D., Nardi A., Pedrotti A., Pratelli N., Rabitti F., Savino P., Sebastiani F., Sperduti G., Thanos C., Trupiano L., Vadicamo L., Vairo C.
The Artificial Intelligence for Media and Humanities laboratory (AIMH) has the mission to investigate and advance the state of the art in the Artificial Intelligence field, specifically addressing applications to digital media and digital humanities, and taking also into account issues related to scalability.This report summarize the 2022 activities of the research group.DOI: 10.32079/isti-ar-2022/002
Metrics:
Aloia N., Amato G., Bartalesi Lenzi V., Benedetti F., Bolettieri P., Cafarelli D., Carrara F., Casarosa V., Ciampi L., Coccomini D. A., Concordia C., Corbara S., Di Benedetto M., Esuli A., Falchi F., Gennaro C., Lagani G., Lenzi E., Meghini C., Messina N., Metilli D., Molinari A., Moreo Fernandez A. D., Nardi A., Pedrotti A., Pratelli N., Rabitti F., Savino P., Sebastiani F., Sperduti G., Thanos C., Trupiano L., Vadicamo L., Vairo C.
The Artificial Intelligence for Media and Humanities laboratory (AIMH) has the mission to investigate and advance the state of the art in the Artificial Intelligence field, specifically addressing applications to digital media and digital humanities, and taking also into account issues related to scalability.This report summarize the 2022 activities of the research group.DOI: 10.32079/isti-ar-2022/002
Metrics:
See at:
CNR IRIS | ISTI Repository | CNR IRIS