2023
Conference article
Open Access
Social and hUman ceNtered XR
Vairo C., Callieri M., Carrara F., Cignoni P., Di Benedetto M., Gennaro C., Giorgi D., Palma G., Vadicamo L., Amato G.
The Social and hUman ceNtered XR (SUN) project is focused on developing eXtended Reality (XR) solutions that integrate the physical and virtual world in a way that is convincing from a human and social perspective. In this paper, we outline the limitations that the SUN project aims to overcome, including the lack of scalable and cost-effective solutions for developing XR applications, limited solutions for mixing the virtual and physical environment, and barriers related to resource limitations of end-user devices. We also propose solutions to these limitations, including using artificial intelligence, computer vision, and sensor analysis to incrementally learn the visual and physical properties of real objects and generate convincing digital twins in the virtual environment. Additionally, the SUN project aims to provide wearable sensors and haptic interfaces to enhance natural interaction with the virtual environment and advanced solutions for user interaction. Finally, we describe three real-life scenarios in which we aim to demonstrate the proposed solutions.Source: Ital-IA 2023 - Workshop su AI per l'industria, Pisa, Italy, 29-31/05/2023
Vairo C., Callieri M., Carrara F., Cignoni P., Di Benedetto M., Gennaro C., Giorgi D., Palma G., Vadicamo L., Amato G.
The Social and hUman ceNtered XR (SUN) project is focused on developing eXtended Reality (XR) solutions that integrate the physical and virtual world in a way that is convincing from a human and social perspective. In this paper, we outline the limitations that the SUN project aims to overcome, including the lack of scalable and cost-effective solutions for developing XR applications, limited solutions for mixing the virtual and physical environment, and barriers related to resource limitations of end-user devices. We also propose solutions to these limitations, including using artificial intelligence, computer vision, and sensor analysis to incrementally learn the visual and physical properties of real objects and generate convincing digital twins in the virtual environment. Additionally, the SUN project aims to provide wearable sensors and haptic interfaces to enhance natural interaction with the virtual environment and advanced solutions for user interaction. Finally, we describe three real-life scenarios in which we aim to demonstrate the proposed solutions.Source: Ital-IA 2023 - Workshop su AI per l'industria, Pisa, Italy, 29-31/05/2023
See at:
ISTI Repository 
| CNR ExploRA
2023
Report
Restricted
SUN D1.1 - Management Website
Amato G., Bolettieri P., Gennaro C., Vadicamo L., Vairo C.
Report describing the online web accessible repository for all project-related documentation, which serves as the primary means for project partners to manage and share documents of the project. https://wiki.sun-xr-project.euSource: ISTI Project Report, SUN, D1.1, 2023
Amato G., Bolettieri P., Gennaro C., Vadicamo L., Vairo C.
Report describing the online web accessible repository for all project-related documentation, which serves as the primary means for project partners to manage and share documents of the project. https://wiki.sun-xr-project.euSource: ISTI Project Report, SUN, D1.1, 2023
See at:
CNR ExploRA 
2022
Conference article
Open Access
AIMH Lab for Cybersecurity
Vairo C., Coccomini D. A., Falchi F., Gennaro C., Massoli F. V., 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 Cy-bersecurity. We discuss about our active research fields, their applications and challenges. We focus on face recognition and detection of adversarial examples and deep fakes. We also present our activities on the detection of persuasion techniques combining image and text analysis.Source: Ital-IA 2022 - Workshop su AI per Cybersecurity, 10/02/2022
Vairo C., Coccomini D. A., Falchi F., Gennaro C., Massoli F. V., 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 Cy-bersecurity. We discuss about our active research fields, their applications and challenges. We focus on face recognition and detection of adversarial examples and deep fakes. We also present our activities on the detection of persuasion techniques combining image and text analysis.Source: Ital-IA 2022 - Workshop su AI per Cybersecurity, 10/02/2022
See at:
ISTI Repository | CNR ExploRA | www.ital-ia2022.it
2022
Journal article
Open Access
Multi-camera vehicle counting using edge-AI
Ciampi L., Gennaro C., Carrara F., Falchi F., Vairo C., Amato G.
This paper presents a novel solution to automatically count vehicles in a parking lot using images captured by smart cameras. Unlike most of the literature on this task, which focuses on the analysis of single images, this paper proposes the use of multiple visual sources to monitor a wider parking area from different perspectives. The proposed multi-camera system is capable of automatically estimating the number of cars present in the entire parking lot directly on board the edge devices. It comprises an on-device deep learning-based detector that locates and counts the vehicles from the captured images and a decentralized geometric-based approach that can analyze the inter-camera shared areas and merge the data acquired by all the devices. We conducted the experimental evaluation on an extended version of the CNRPark-EXT dataset, a collection of images taken from the parking lot on the campus of the National Research Council (CNR) in Pisa, Italy. We show that our system is robust and takes advantage of the redundant information deriving from the different cameras, improving the overall performance without requiring any extra geometrical information of the monitored scene.Source: Expert systems with applications (2022). doi:10.1016/j.eswa.2022.117929
DOI: 10.1016/j.eswa.2022.117929
Project(s): AI4EU
, AI4Media
Metrics:
Ciampi L., Gennaro C., Carrara F., Falchi F., Vairo C., Amato G.
This paper presents a novel solution to automatically count vehicles in a parking lot using images captured by smart cameras. Unlike most of the literature on this task, which focuses on the analysis of single images, this paper proposes the use of multiple visual sources to monitor a wider parking area from different perspectives. The proposed multi-camera system is capable of automatically estimating the number of cars present in the entire parking lot directly on board the edge devices. It comprises an on-device deep learning-based detector that locates and counts the vehicles from the captured images and a decentralized geometric-based approach that can analyze the inter-camera shared areas and merge the data acquired by all the devices. We conducted the experimental evaluation on an extended version of the CNRPark-EXT dataset, a collection of images taken from the parking lot on the campus of the National Research Council (CNR) in Pisa, Italy. We show that our system is robust and takes advantage of the redundant information deriving from the different cameras, improving the overall performance without requiring any extra geometrical information of the monitored scene.Source: Expert systems with applications (2022). doi:10.1016/j.eswa.2022.117929
DOI: 10.1016/j.eswa.2022.117929
Project(s): AI4EU
, AI4Media Metrics:
See at:
ISTI Repository | CNR ExploRA | www.sciencedirect.com
2022
Conference article
Open Access
VISIONE at Video Browser Showdown 2022
Amato G., Bolettieri P., Carrara F., Falchi F., Gennaro C., Messina N., Vadicamo L., Vairo C.
VISIONE is a content-based retrieval system that supports various search functionalities (text search, object/color-based search, semantic and visual similarity search, temporal search). It uses a full-text search engine as a search backend. In the latest version of our system, we modified the user interface, and we made some changes to the techniques used to analyze and search for videos.Source: MMM 2022 - 28th International Conference on Multimedia Modeling, pp. 543–548, Phu Quoc, Vietnam, 06-10/06/2022
DOI: 10.1007/978-3-030-98355-0_52
Project(s): AI4EU, AI4Media
Metrics:
Amato G., Bolettieri P., Carrara F., Falchi F., Gennaro C., Messina N., Vadicamo L., Vairo C.
VISIONE is a content-based retrieval system that supports various search functionalities (text search, object/color-based search, semantic and visual similarity search, temporal search). It uses a full-text search engine as a search backend. In the latest version of our system, we modified the user interface, and we made some changes to the techniques used to analyze and search for videos.Source: MMM 2022 - 28th International Conference on Multimedia Modeling, pp. 543–548, Phu Quoc, Vietnam, 06-10/06/2022
DOI: 10.1007/978-3-030-98355-0_52
Project(s): AI4EU
, AI4Media Metrics:
See at:
ISTI Repository | doi.org | link.springer.com | CNR ExploRA
2022
Contribution to conference
Open Access
AI and computer vision for smart cities
Amato G., Carrara F., Ciampi L., Di Benedetto M., Gennaro C., Falchi F., Messina N., Vairo C.
Artificial Intelligence (AI) is increasingly employed to develop public services that make life easier for citizens. In this abstract, we present some research topics and applications carried out by the Artificial Intelligence for Media and Humanities (AIMH) laboratory of the ISTI-CNR of Pisa about the study and development of AI-based services for Smart Cities dedicated to the interaction with the physical world through the analysis of images gathered from city cameras. Like no other sensing mechanism, networks of city cameras can 'observe' the world and simultaneously provide visual data to AI systems to extract relevant information and make/suggest decisions helping to solve many real-world problems. Specifically, we discuss some solutions in the context of smart mobility, parking monitoring, infrastructure management, and surveillance systems.Source: I-CiTies 2022 - 8th Italian Conference on ICT for Smart Cities And Communities, Ascoli Piceno, Italy, 14-16/09/2022
Project(s): AI4Media
Amato G., Carrara F., Ciampi L., Di Benedetto M., Gennaro C., Falchi F., Messina N., Vairo C.
Artificial Intelligence (AI) is increasingly employed to develop public services that make life easier for citizens. In this abstract, we present some research topics and applications carried out by the Artificial Intelligence for Media and Humanities (AIMH) laboratory of the ISTI-CNR of Pisa about the study and development of AI-based services for Smart Cities dedicated to the interaction with the physical world through the analysis of images gathered from city cameras. Like no other sensing mechanism, networks of city cameras can 'observe' the world and simultaneously provide visual data to AI systems to extract relevant information and make/suggest decisions helping to solve many real-world problems. Specifically, we discuss some solutions in the context of smart mobility, parking monitoring, infrastructure management, and surveillance systems.Source: I-CiTies 2022 - 8th Italian Conference on ICT for Smart Cities And Communities, Ascoli Piceno, Italy, 14-16/09/2022
Project(s): AI4Media
See at:
icities2022.unicam.it | ISTI Repository | ISTI Repository | CNR ExploRA
2022
Other
Open Access
COCO, LVIS, Open Images V4 classes mapping
Amato G., Bolettieri P., Carrara F., Falchi F., Gennaro C., Messina N., Vadicamo L., Vairo C.
This repository contains a mapping between the classes of COCO, LVIS, and Open Images V4 datasets into a unique set of 1460 classes. COCO [Lin et al 2014] contains 80 classes, LVIS [gupta2019lvis] contains 1460 classes, Open Images V4 [Kuznetsova et al. 2020] contains 601 classes. We built a mapping of these classes using a semi-automatic procedure in order to have a unique final list of 1460 classes. We also generated a hierarchy for each class, using wordnet.Project(s): AI4Media
Amato G., Bolettieri P., Carrara F., Falchi F., Gennaro C., Messina N., Vadicamo L., Vairo C.
This repository contains a mapping between the classes of COCO, LVIS, and Open Images V4 datasets into a unique set of 1460 classes. COCO [Lin et al 2014] contains 80 classes, LVIS [gupta2019lvis] contains 1460 classes, Open Images V4 [Kuznetsova et al. 2020] contains 601 classes. We built a mapping of these classes using a semi-automatic procedure in order to have a unique final list of 1460 classes. We also generated a hierarchy for each class, using wordnet.Project(s): AI4Media
See at:
CNR ExploRA | zenodo.org
2022
Report
Open Access
AIMH research activities 2022
Aloia N., Amato G., Bartalesi 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 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 2022 activities of the research group.Source: ISTI Annual reports, 2022
DOI: 10.32079/isti-ar-2022/002
Metrics:
Aloia N., Amato G., Bartalesi 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 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 2022 activities of the research group.Source: ISTI Annual reports, 2022
DOI: 10.32079/isti-ar-2022/002
Metrics:
See at:
ISTI Repository | CNR ExploRA
2022
Software
Unknown
Visione IV
Amato G., Bolettieri P., Carrara F., Falchi F., Gennaro C., Messina N., Vadicamo L., Vairo C.
VISIONE IV is the fourth release of a tool for fast and effective video search on a large-scale dataset. It includes several search functionalities like text search, object and color-based search, semantic and visual similarity search, and temporal search.
Amato G., Bolettieri P., Carrara F., Falchi F., Gennaro C., Messina N., Vadicamo L., Vairo C.
VISIONE IV is the fourth release of a tool for fast and effective video search on a large-scale dataset. It includes several search functionalities like text search, object and color-based search, semantic and visual similarity search, and temporal search.
See at: CNR ExploRA | visione.isti.cnr.it
2022
Software
Unknown
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 ExploRA
2022
Software
Unknown
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 ExploRA
2022
Software
Unknown
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 ExploRA
2022
Software
Unknown
VisioneRAI
Amato G., Bolettieri P., Carrara F., Falchi F., Gennaro C., Messina N., Vadicamo L., Vairo C.
A release of the VISIONE tool on RAI's real media content. This first prototipe was developed as part of AI4media European project , and provides a set of integrated components for browsing and searching videos by similar frames, by objects occurring in videos, by spatial relationships among objects in videos, and by cross-model search functionality (text-to-video search).Project(s): AI4Media
Amato G., Bolettieri P., Carrara F., Falchi F., Gennaro C., Messina N., Vadicamo L., Vairo C.
A release of the VISIONE tool on RAI's real media content. This first prototipe was developed as part of AI4media European project , and provides a set of integrated components for browsing and searching videos by similar frames, by objects occurring in videos, by spatial relationships among objects in videos, and by cross-model search functionality (text-to-video search).Project(s): AI4Media
See at: CNR ExploRA | visionerai.isti.cnr.it
2022
Software
Unknown
Extension image recognition system
Bolettieri P., Vairo C.
Image recognition system realised for the ESA Extension project. The system is able to visually recognise the main monuments of the city of L'Aquila.
Bolettieri P., Vairo C.
Image recognition system realised for the ESA Extension project. The system is able to visually recognise the main monuments of the city of L'Aquila.
See at: gitea-s2i2s.isti.cnr.it | CNR ExploRA
2021
Journal article
Open Access
The VISIONE video search system: exploiting off-the-shelf text search engines for large-scale video retrieval
Amato G., Bolettieri P., Carrara F., Debole F., Falchi F., Gennaro C., Vadicamo L., Vairo C.
This paper describes in detail VISIONE, a video search system that allows users to search for videos using textual keywords, the occurrence of objects and their spatial relationships, the occurrence of colors and their spatial relationships, and image similarity. These modalities can be combined together to express complex queries and meet users' needs. The peculiarity of our approach is that we encode all information extracted from the keyframes, such as visual deep features, tags, color and object locations, using a convenient textual encoding that is indexed in a single text retrieval engine. This offers great flexibility when results corresponding to various parts of the query (visual, text and locations) need to be merged. In addition, we report an extensive analysis of the retrieval performance of the system, using the query logs generated during the Video Browser Showdown (VBS) 2019 competition. This allowed us to fine-tune the system by choosing the optimal parameters and strategies from those we tested.Source: JOURNAL OF IMAGING 7 (2021). doi:10.3390/jimaging7050076
DOI: 10.3390/jimaging7050076
DOI: 10.48550/arxiv.2008.02749
Project(s): AI4Media
Metrics:
Amato G., Bolettieri P., Carrara F., Debole F., Falchi F., Gennaro C., Vadicamo L., Vairo C.
This paper describes in detail VISIONE, a video search system that allows users to search for videos using textual keywords, the occurrence of objects and their spatial relationships, the occurrence of colors and their spatial relationships, and image similarity. These modalities can be combined together to express complex queries and meet users' needs. The peculiarity of our approach is that we encode all information extracted from the keyframes, such as visual deep features, tags, color and object locations, using a convenient textual encoding that is indexed in a single text retrieval engine. This offers great flexibility when results corresponding to various parts of the query (visual, text and locations) need to be merged. In addition, we report an extensive analysis of the retrieval performance of the system, using the query logs generated during the Video Browser Showdown (VBS) 2019 competition. This allowed us to fine-tune the system by choosing the optimal parameters and strategies from those we tested.Source: JOURNAL OF IMAGING 7 (2021). doi:10.3390/jimaging7050076
DOI: 10.3390/jimaging7050076
DOI: 10.48550/arxiv.2008.02749
Project(s): AI4Media
Metrics:
See at:
arXiv.org e-Print Archive | Journal of Imaging | Journal of Imaging | ISTI Repository | ISTI Repository | CNR ExploRA | DOAJ-Articles | www.mdpi.com | Journal of Imaging | ZENODO | doi.org
2021
Conference article
Open Access
VISIONE at Video Browser Showdown 2021
Amato G., Bolettieri P., Falchi F., Gennaro C., Messina N., Vadicamo L., Vairo C.
This paper presents the second release of VISIONE, a tool for effective video search on large-scale collections. It allows users to search for videos using textual descriptions, keywords, occurrence of objects and their spatial relationships, occurrence of colors and their spatial relationships, and image similarity. One of the main features of our system is that it employs specially designed textual encodings for indexing and searching video content using the mature and scalable Apache Lucene full-text search engine.Source: MMM 2021 - 27th International Conference on Multimedia Modeling, pp. 473–478, Prague, Czech Republic, 22-24/06/2021
DOI: 10.1007/978-3-030-67835-7_47
Project(s): AI4EU, AI4Media
Metrics:
Amato G., Bolettieri P., Falchi F., Gennaro C., Messina N., Vadicamo L., Vairo C.
This paper presents the second release of VISIONE, a tool for effective video search on large-scale collections. It allows users to search for videos using textual descriptions, keywords, occurrence of objects and their spatial relationships, occurrence of colors and their spatial relationships, and image similarity. One of the main features of our system is that it employs specially designed textual encodings for indexing and searching video content using the mature and scalable Apache Lucene full-text search engine.Source: MMM 2021 - 27th International Conference on Multimedia Modeling, pp. 473–478, Prague, Czech Republic, 22-24/06/2021
DOI: 10.1007/978-3-030-67835-7_47
Project(s): AI4EU
, AI4Media Metrics:
See at:
ISTI Repository | ZENODO | zenodo.org | Lecture Notes in Computer Science | link.springer.com | CNR ExploRA
2021
Report
Open Access
AIMH research activities 2021
Aloia N., Amato G., Bartalesi 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 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.Source: ISTI Annual Report, ISTI-2021-AR/003, pp.1–34, 2021
DOI: 10.32079/isti-ar-2021/003
Metrics:
Aloia N., Amato G., Bartalesi 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 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.Source: ISTI Annual Report, ISTI-2021-AR/003, pp.1–34, 2021
DOI: 10.32079/isti-ar-2021/003
Metrics:
See at:
ISTI Repository | CNR ExploRA
2021
Contribution to conference
Open Access
A multi-camera solution for counting vehicles on the edge
Ciampi L., Gennaro C., Carrara F., Falchi F., Vairo C., Amato G.
Smart mobility applications, such as intelligent parking and road traffic management, are nowadays widely employed worldwide, making our cities more livable, bringing benefits to our lives, reducing costs, and improving energy usage. We propose a multi-camera system to automatically count vehicles in a parking lot using images captured by smart cameras. Unlike most of the literature on this task, which focuses on the analysis of single images, this paper proposes the use of multiple visual sources to monitor a wider parking area from different perspectives. Experiments show that our solution is robust, flexible, and can benefit from redundant information from different cameras while improving overall performance.Source: I-CiTies 2021 - 7th Italian Conference on ICT for Smart Cities And Communities, Online Conference, 22-24/09/2021
Project(s): AI4EU, AI4Media
Ciampi L., Gennaro C., Carrara F., Falchi F., Vairo C., Amato G.
Smart mobility applications, such as intelligent parking and road traffic management, are nowadays widely employed worldwide, making our cities more livable, bringing benefits to our lives, reducing costs, and improving energy usage. We propose a multi-camera system to automatically count vehicles in a parking lot using images captured by smart cameras. Unlike most of the literature on this task, which focuses on the analysis of single images, this paper proposes the use of multiple visual sources to monitor a wider parking area from different perspectives. Experiments show that our solution is robust, flexible, and can benefit from redundant information from different cameras while improving overall performance.Source: I-CiTies 2021 - 7th Italian Conference on ICT for Smart Cities And Communities, Online Conference, 22-24/09/2021
Project(s): AI4EU
, AI4Media
See at:
ISTI Repository | ISTI Repository | CNR ExploRA | www.icities2021.unisa.it
2021
Software
Unknown
Visione III
Amato G., Bolettieri P., Carrara F., Falchi F., Gennaro C., Messina N., Vadicamo L., Vairo C.
VISIONE III is a content-based retrieval system that supports various search functionalities (text search, object/color-based search, semantic and visual similarity search, temporal search). It uses a full-text search engine as a search backend. In this third version of our system, we modified the user interface, and we made some changes to the techniques used to analyze and search for videos.
Amato G., Bolettieri P., Carrara F., Falchi F., Gennaro C., Messina N., Vadicamo L., Vairo C.
VISIONE III is a content-based retrieval system that supports various search functionalities (text search, object/color-based search, semantic and visual similarity search, temporal search). It uses a full-text search engine as a search backend. In this third version of our system, we modified the user interface, and we made some changes to the techniques used to analyze and search for videos.
See at: bilioso.isti.cnr.it | CNR ExploRA
2020
Conference article
Open Access
Edge-Based Video Surveillance with Embedded Devices
Kavalionak H., Gennaro C., Amato G., Vairo C., Perciante C., Meghini C., Falchi F., Rabitti F.
Video surveillance systems have become indispensable tools for the security and organization of public and private areas. In this work, we propose a novel distributed protocol for an edge-based face recogni-tion system that takes advantage of the computational capabilities of the surveillance devices (i.e., cameras) to perform person recognition. The cameras fall back to a centralized server if their hardware capabili-ties are not enough to perform the recognition. We evaluate the proposed algorithm via extensive experiments on a freely available dataset. As a prototype of surveillance embedded devices, we have considered a Rasp-berry PI with the camera module. Using simulations, we show that our algorithm can reduce up to 50% of the load of the server with no negative impact on the quality of the surveillance service.Source: 28th Symposium on Advanced Database Systems (SEBD), pp. 278–285, Villasimius, Sardinia, Italy, 21-24/06/2020
Kavalionak H., Gennaro C., Amato G., Vairo C., Perciante C., Meghini C., Falchi F., Rabitti F.
Video surveillance systems have become indispensable tools for the security and organization of public and private areas. In this work, we propose a novel distributed protocol for an edge-based face recogni-tion system that takes advantage of the computational capabilities of the surveillance devices (i.e., cameras) to perform person recognition. The cameras fall back to a centralized server if their hardware capabili-ties are not enough to perform the recognition. We evaluate the proposed algorithm via extensive experiments on a freely available dataset. As a prototype of surveillance embedded devices, we have considered a Rasp-berry PI with the camera module. Using simulations, we show that our algorithm can reduce up to 50% of the load of the server with no negative impact on the quality of the surveillance service.Source: 28th Symposium on Advanced Database Systems (SEBD), pp. 278–285, Villasimius, Sardinia, Italy, 21-24/06/2020
See at:
ceur-ws.org | ISTI Repository | CNR ExploRA