Domain adaptation for traffic density estimation
Ciampi L., Santiago C., Costeira J. P., Gennaro C., Amato G.
Convolutional Neural Networks have produced state-of-the-art results for a multitude of computer vision tasks under supervised learning. However, the crux of these methods is the need for a massive amount of labeled data to guarantee that they generalize well to diverse testing scenarios. In many real-world applications, there is indeed a large domain shift between the distributions of the train (source) and test (target) domains, leading to a significant drop in performance at inference time. Unsupervised Domain Adaptation (UDA) is a class of techniques that aims to mitigate this drawback without the need for labeled data in the target domain. This makes it particularly useful for the tasks in which acquiring new labeled data is very expensive, such as for semantic and instance segmentation. In this work, we propose an end-to-end CNN-based UDA algorithm for traffic density estimation and counting, based on adversarial learning in the output space. The density estimation is one of those tasks requiring per-pixel annotated labels and, therefore, needs a lot of human effort. We conduct experiments considering different types of domain shifts, and we make publicly available two new datasets for the vehicle counting task that were also used for our tests. One of them, the Grand Traffic Auto dataset, is a synthetic collection of images, obtained using the graphical engine of the Grand Theft Auto video game, automatically annotated with precise per-pixel labels. Experiments show a significant improvement using our UDA algorithm compared to the model's performance without domain adaptation.Source: VISIGRAPP 2021 - 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, pp. 185–195, Online Conference, 08-10 February, 2021
DOI: 10.5220/0010303401850195
Project(s): AI4EU 
, AI4Media
See at:
ISTI Repository 
| CNR ExploRA | www.scitepress.org
Unsupervised vehicle counting via multiple camera domain adaptation
Ciampi L., Santiago C., Costeira J. P., Gennaro C., Amato G.
Monitoring vehicle flow in cities is a crucial issue to improve the urban environment and quality of life of citizens. Images are the best sensing modality to perceive and asses the flow of vehicles in large areas. Current technologies for vehicle counting in images hinge on large quantities of annotated data, preventing their scalability to city-scale as new cameras are added to the system. This is a recurrent problem when dealing with physical systems and a key research area in Machine Learning and AI. We propose and discuss a new methodology to design image-based vehicle density estimators with few labeled data via multiple camera domain adaptations.Source: ECAI-2020 - 1st International Workshop on New Foundations for Human-Centered AI (NeHuAI), pp. 1–4, Online Conference, 04 September, 2020
Project(s): AI4EU
See at:
ceur-ws.org | ISTI Repository | CNR ExploRA
Virtual to real adaptation of pedestrian detectors
Ciampi L., Messina N., Falchi F., Gennaro C., Amato G.
Pedestrian detection through Computer Vision is a building block for a multitude of applications. Recently, there has been an increasing interest in convolutional neural network-based architectures to execute such a task. One of these supervised networks' critical goals is to generalize the knowledge learned during the training phase to new scenarios with different characteristics. A suitably labeled dataset is essential to achieve this purpose. The main problem is that manually annotating a dataset usually requires a lot of human effort, and it is costly. To this end, we introduce ViPeD (Virtual Pedestrian Dataset), a new synthetically generated set of images collected with the highly photo-realistic graphical engine of the video game GTA V (Grand Theft Auto V), where annotations are automatically acquired. However, when training solely on the synthetic dataset, the model experiences a Synthetic2Real domain shift leading to a performance drop when applied to real-world images. To mitigate this gap, we propose two different domain adaptation techniques suitable for the pedestrian detection task, but possibly applicable to general object detection. Experiments show that the network trained with ViPeD can generalize over unseen real-world scenarios better than the detector trained over real-world data, exploiting the variety of our synthetic dataset. Furthermore, we demonstrate that with our domain adaptation techniques, we can reduce the Synthetic2Real domain shift, making the two domains closer and obtaining a performance improvement when testing the network over the real-world images.Source: Sensors (Basel) 20 (2020). doi:10.3390/s20185250
DOI: 10.3390/s20185250
See at:
Sensors | arXiv.org e-Print Archive | Sensors | Europe PubMed Central | ISTI Repository | CNR ExploRA | Sensors | Sensors | Sensors | Sensors
AIMH research activities 2020
Aloia N., Amato G., Bartalesi V., Benedetti F., Bolettieri P., Carrara F., Casarosa V., Ciampi L., Concordia C., Corbara S., 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., Thanos C., Trupiano L., Vadicamo L., Vairo C.
Annual Report of the Artificial Intelligence for Media and Humanities laboratory (AIMH) research activities in 2020.
See at:
ISTI Repository | CNR ExploRA
Monitoring Traffic Flows via Unsupervised Domain Adaptation
Ciampi L., Gennaro C., Amato G.
Monitoring traffic flows in cities is crucial to improve urban mobility, and images are the best sensing modality to perceive and assess the flow of vehicles in large areas. However, current machine learning-based technologies using images hinge on large quantities of annotated data, preventing their scalability to city-scale as new cameras are added to the system. We propose a new methodology to design image-based vehicle density estimators with few labeled data via an unsupervised domain adaptation technique.Source: 6th Italian Conference on ICT for Smart Cities And Communities, pp. 1–2, Online Conference, 23-25/09/2020,
Project(s): AI4EU
See at:
ISTI Repository | CNR ExploRA
SmartPark@Lucca - D5. Integrazione e sperimentazione sul campo
Amato G., Bolettieri P., Carrara F., Ciampi L., Gennaro C., Leone G. R., Moroni D., Pieri G., Vairo C.
In questo deliverable sono descritte le attività eseguite all'interno del WP3, in particolare relative al Task 3.1 - Integrazione e al Task 3.2 - Sperimentazione sul campo.Source: Project report, SmartPark@Lucca, Deliverable D5, pp.1–24, 2019
See at:
ISTI Repository | CNR ExploRA
Intelligenza Artificiale e Analisi Visuale per la Cyber Security
Vairo C., Amato G., Ciampi L., Falchi F., Gennaro C., Massoli F. V.
Negli ultimi anni la Cyber Security ha acquisito una connotazione sempre più vasta, andando oltre la concezione di semplice sicurezza dei sistemi informatici e includendo anche la sorveglianza e la sicurezza in senso lato, sfruttando le ultime tecnologie come ad esempio l'intelligenza artificiale. In questo contributo vengono presentate le principali attività di ricerca e alcune delle tecnologie utilizzate e sviluppate dal gruppo di ricerca AIMIR dell'ISTI-CNR, e viene fornita una panoramica dei progetti di ricerca, sia passati che attualmente attivi, in cui queste tecnologie di intelligenza artificiale vengono utilizzare per lo sviluppo di applicazioni e servizi per la Cyber Security.Source: Ital-IA, Roma, 18/3/2019, 19/3/2019
See at:
ISTI Repository | CNR ExploRA | www.ital-ia.it
Learning pedestrian detection from virtual worlds
Amato G., Ciampi L., Falchi F., Gennaro C., Messina N.
In this paper, we present a real-time pedestrian detection system that has been trained using a virtual environment. This is a very popular topic of research having endless practical applications and recently, there was an increasing interest in deep learning architectures for performing such a task. However, the availability of large labeled datasets is a key point for an effective train of such algorithms. For this reason, in this work, we introduced ViPeD, a new synthetically generated set of images extracted from a realistic 3D video game where the labels can be automatically generated exploiting 2D pedestrian positions extracted from the graphics engine. We exploited this new synthetic dataset fine-tuning a state-of-the-art computationally efficient Convolutional Neural Network (CNN). A preliminary experimental evaluation, compared to the performance of other existing approaches trained on real-world images, shows encouraging results.Source: Image Analysis and Processing - ICIAP 2019, pp. 302–312, Trento, Italia, 9/9/2019, 13/9/2019
DOI: 10.1007/978-3-030-30642-7_27
Project(s): AI4EU
See at:
ISTI Repository | academic.microsoft.com 
| dblp.uni-trier.de | doi.org | link.springer.com | link.springer.com | CNR ExploRA | rd.springer.com
Parking Lot Monitoring with Smart Cameras
Amato G., Bolettieri P., Carrara F., Ciampi L., Gennaro C., Leone G. R., Moroni D., Pieri G., Vairo C.
In this article, we present a scenario for monitoring the occupancy of parking spaces in the historical city of Lucca (Italy) based on the use of intelligent cameras and the most modern technologies of artificial intelligence.
The system is designed to use different smart-camera prototypes: where the connection to the power grid is available, we propose a powerful embedded hardware solution that exploits a Deep Neural Network. Otherwise, a fully autonomous energy-harvesting node based on a low-energy custom board employing lightweight image analysis algorithms is considered.Source: 5th Italian Conference on ICT for Smart Cities And Communities, pp. 1–3, Pisa, Italy, 18-20 September, 2019
See at:
ISTI Repository | CNR ExploRA
AIMIR 2019 Research Activities
Amato G., Bolettieri P., Carrara F., Ciampi L., Di Benedetto M., Debole F., Falchi F., Gennaro C., Lagani G., Massoli F. V., Messina N., Rabitti F., Savino P., Vadicamo L., Vairo C.
Multimedia Information Retrieval (AIMIR) research group is part of the NeMIS laboratory of the Information Science and Technologies Institute "A. Faedo" (ISTI) of the Italian National Research Council (CNR). The AIMIR group has a long experience in topics related to: Artificial Intelligence, Multimedia Information Retrieval, Computer Vision and Similarity search on a large scale.
We aim at investigating the use of Artificial Intelligence and Deep Learning, for Multimedia Information Retrieval, addressing both effectiveness and efficiency. Multimedia information retrieval techniques should be able to provide users with pertinent results, fast, on huge amount of multimedia data.
Application areas of our research results range from cultural heritage to smart tourism, from security to smart cities, from mobile visual search to augmented reality.
This report summarize the 2019 activities of the research group.Source: AIMIR Annual Report, 2019
See at:
ISTI Repository | CNR ExploRA
Counting vehicles with deep learning in onboard UAV imagery
Amato G., Ciampi L., Falchi F., Gennaro C.
The integration of mobile and ubiquitous computing with deep learning methods is a promising emerging trend that aims at moving the processing task closer to the data source rather than bringing the data to a central node. The advantages of this approach range from bandwidth reduction, high scalability, to high reliability, just to name a few. In this paper, we propose a real-time deep learning approach to automatically detect and count vehicles in videos taken from a UAV (Unmanned Aerial Vehicle). Our solution relies on a convolutional neural network-based model fine-tuned to the specific domain of applications that is able to precisely localize instances of the vehicles using a regression approach, straight from image pixels to bounding box coordinates, reasoning globally about the image when making predictions and implicitly encoding contextual information. A comprehensive experimental evaluation on real-world datasets shows that our approach results in state-of-the-art performances. Furthermore, our solution achieves real-time performances by running at a speed of 4 Frames Per Second on an NVIDIA Jetson TX2 board, showing the potentiality of this approach for real-time processing in UAVs.Source: ISCC 2019 - IEEE Symposium on Computers and Communications, pp. 1–6, Barcelona , Spain, 30 June 2019 - 03 July 2019
DOI: 10.1109/iscc47284.2019.8969620
See at:
ISTI Repository | academic.microsoft.com | dblp.uni-trier.de | ieeexplore.ieee.org | CNR ExploRA | xplorestaging.ieee.org
SmartPark@Lucca - D4. Progettazione e realizzazione software di riconoscimento visuale parcheggi
Amato G., Bolettieri P., Carrara F., Ciampi L., Gennaro C., Leone G. R., Moroni D., Pieri G., Vairo C.
In questo deliverable sono descritte le attività eseguite all'interno del WP2, in particolare relative al Task 2.3 - Realizzazione SW.Source: Project report, SmartPark@Lucca, Deliverable D4, pp.1–15, 2018
See at:
ISTI Repository | CNR ExploRA
A wireless smart camera network for parking monitoring
Amato G., Bolettieri P., Carrara F., Ciampi L., Gennaro C., Leone G. R., Moroni D., Pieri G., Vairo C.
In this paper we present a Wireless Sensor Network (WSN), which is intended to provide a scalable solution for active cooperative monitoring of wide geographical areas. The system is designed to use different smart-camera prototypes: where the connection to the power grid is available a powerful embedded hardware implements a Deep Neural Network, otherwise a fully autonomous energy-harvesting node based on a low-energy custom board employs lightweight image analysis algorithms. Parking lots occupancy monitoring in the historical city of Lucca (Italy) is the application where the implemented smart cameras have been deployed. Traffic monitoring and surveillance are possible new scenarios for the system.Source: 2018 IEEE Globecom Workshops (GC Workshops), pp. 1–6, Abu Dhabi, United Arab Emirates, United Arab Emirates, 9-13 December 2018
DOI: 10.1109/glocomw.2018.8644226
See at:
ISTI Repository | academic.microsoft.com | dblp.uni-trier.de | ieeexplore.ieee.org | CNR ExploRA | xplorestaging.ieee.org
Counting Vehicles with Cameras
Ciampi L., Amato G., Falchi F., Gennaro C., Rabitti F.
This paper aims to develop a method that can accurately count vehicles from images of parking areas captured by smart cameras. To this end, we have proposed a deep learning-based approach for car detection that permits the input images to be of arbitrary perspectives, illumination, and occlusions. No other information about the scenes is needed, such as the position of the parking lots or the perspective maps. This solution is tested using Counting CNRPark-EXT, a new dataset created for this specific task and that is another contribution to our research. Our experiments show that our solution outperforms the state-of-the-art approaches.Source: SEBD 2018, pp. 1–8, Castellaneta Marina - Taranto - Italy, 24/06/2018, 27/06/2018
See at:
ceur-ws.org | ISTI Repository | CNR ExploRA