2023 Conference article Open Access

CrowdSim2: an open synthetic benchmark for object detectors
Foszner P., Szczesna A., Ciampi L., Messina N., Cygan A., Bizon B., Cogiel M., Golba D., Macioszek E., Staniszewski M.
Data scarcity has become one of the main obstacles to developing supervised models based on Artificial Intelligence in Computer Vision. Indeed, Deep Learning-based models systematically struggle when applied in new scenarios never seen during training and may not be adequately tested in non-ordinary yet crucial real-world situations. This paper presents and publicly releases CrowdSim2, a new synthetic collection of images suitable for people and vehicle detection gathered from a simulator based on the Unity graphical engine. It consists of thousands of images gathered from various synthetic scenarios resembling the real world, where we varied some factors of interest, such as the weather conditions and the number of objects in the scenes. The labels are automatically collected and consist of bounding boxes that precisely localize objects belonging to the two object classes, leaving out humans from the annotation pipeline. We exploited this new benchmark as a testing ground for some state-of-the-art detectors, showing that our simulated scenarios can be a valuable tool for measuring their performances in a controlled environment.Source: VISIGRAPP 2023 - 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, pp. 676–683, Lisbon, Portugal, 19-21/02/2023
DOI: 10.5220/0011692500003417
Project(s): AI4Media
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See at: ISTI Repository | www.scitepress.org | CNR ExploRA

2023 Conference article Open Access

Development of a realistic crowd simulation environment for fine-grained validation of people tracking methods
Foszner P., Szczesna A., Ciampi L., Messina N., Cygan A., Bizon B., Cogiel M., Golba D., Macioszek E., Staniszewski M.
Generally, crowd datasets can be collected or generated from real or synthetic sources. Real data is generated by using infrastructure-based sensors (such as static cameras or other sensors). The use of simulation tools can significantly reduce the time required to generate scenario-specific crowd datasets, facilitate data-driven research, and next build functional machine learning models. The main goal of this work was to develop an extension of crowd simulation (named CrowdSim2) and prove its usability in the application of people-tracking algorithms. The simulator is developed using the very popular Unity 3D engine with particular emphasis on the aspects of realism in the environment, weather conditions, traffic, and the movement and models of individual agents. Finally, three methods of tracking were used to validate generated dataset: IOU-Tracker, Deep-Sort, and Deep-TAMA.Source: VISIGRAPP 2023 - 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, pp. 222–229, Lisbon, Portugal, 19-21/02/2023
DOI: 10.5220/0011691500003417
Project(s): AI4Media
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See at: ISTI Repository | www.scitepress.org | CNR ExploRA

2019 Conference article Open Access

See at: ISTI Repository | doi.org | ieeexplore.ieee.org | CNR ExploRA

2020 Conference article Open Access

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

2021 Conference article Open Access

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
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2020 Contribution to conference Open Access

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: I-Cities 2020 - 6th Italian Conference on ICT for Smart Cities And Communities, Online Conference, 23-25/09/2020
Project(s): AI4EU

See at: icities2020.unisa.it | ISTI Repository | ISTI Repository | CNR ExploRA

2021 Conference article Open Access

Traffic density estimation via unsupervised domain adaptation
Ciampi L., Santiago C., Costeira J. P., 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: SEBD 2021 - Italian Symposium on Advanced Database Systems, pp. 442–449, Pizzo Calabro, Italy, 05-09/09/2021
Project(s): AI4EU

See at: ceur-ws.org | ISTI Repository | CNR ExploRA

2022 Conference article Open Access

Counting or localizing? Evaluating cell counting and detection in microscopy images
Ciampi L., Carrara F., Amato G., Gennaro C.
Image-based automatic cell counting is an essential yet challenging task, crucial for the diagnosing of many diseases. Current solutions rely on Convolutional Neural Networks and provide astonishing results. However, their performance is often measured only considering counting errors, which can lead to masked mistaken estimations; a low counting error can be obtained with a high but equal number of false positives and false negatives. Consequently, it is hard to determine which solution truly performs best. In this work, we investigate three general counting approaches that have been successfully adopted in the literature for counting several different categories of objects. Through an experimental evaluation over three public collections of microscopy images containing marked cells, we assess not only their counting performance compared to several state-of-the-art methods but also their ability to correctly localize the counted cells. We show that commonly adopted counting metrics do not always agree with the localization performance of the tested models, and thus we suggest integrating the proposed evaluation protocol when developing novel cell counting solutions.Source: VISIGRAPP 2022 - 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, pp. 887–897, Online conference, 6-8/2/2022
DOI: 10.5220/0010923000003124
Project(s): AI4Media
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2022 Doctoral thesis Open Access

See at: etd.adm.unipi.it | ISTI Repository | CNR ExploRA

2022 Dataset Open Access

Night and day instance segmented park (NDISPark) dataset: a collection of images taken by day and by night for vehicle detection, segmentation and counting in parking areas
Ciampi L., Santiago C., Costeira J. P., Gennaro C., Amato G.
NDIS Park is a collection of images of parking lots for vehicle detection, segmentation, and counting. Each image is manually labeled with pixel-wise masks and bounding boxes localizing vehicle instances. The dataset includes 259 images depicting several parking areas describing most of the problematic situations that we can find in a real scenario: seven different cameras capture the images under various weather conditions and viewing angles. Another challenging aspect is the presence of partial occlusion patterns in many scenes such as obstacles (trees, lampposts, other cars) and shadowed cars. The main peculiarity is that images are taken during the day and the night, showing utterly different lighting conditions.Project(s): AI4EU , AI4Media

See at: ISTI Repository | CNR ExploRA | zenodo.org

2022 Conference article Open Access

A spatio-temporal attentive network for video-based crowd counting
Avvenuti M., Bongiovanni M., Ciampi L., Falchi F., Gennaro C., Messina N.
Automatic people counting from images has recently drawn attention for urban monitoring in modern Smart Cities due to the ubiquity of surveillance camera networks. Current computer vision techniques rely on deep learning-based algorithms that estimate pedestrian densities in still, individual images. Only a bunch of works take advantage of temporal consistency in video sequences. In this work, we propose a spatio-temporal attentive neural network to estimate the number of pedestrians from surveillance videos. By taking advantage of the temporal correlation between consecutive frames, we lowered state-of-the-art count error by 5% and localization error by 7.5% on the widely-used FDST benchmark.Source: ISCC 2022 - 27th IEEE Symposium on Computers and Communications, Rhodes Island, Greece, 30/06/2022-03/07/2022
DOI: 10.1109/iscc55528.2022.9913019
Project(s): AI4Media
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2023 Conference article Open Access

Unsupervised domain adaptation for video violence detection in the wild
Ciampi L., Santiago C., Costeira J. P., Falchi F. Gennaro C., Amato G.
Video violence detection is a subset of human action recognition aiming to detect violent behaviors in trimmed video clips. Current Computer Vision solutions based on Deep Learning approaches provide astonishing results. However, their success relies on large collections of labeled datasets for supervised learning to guarantee that they generalize well to diverse testing scenarios. Although plentiful annotated data may be available for some pre-specified domains, manual annotation is unfeasible for every ad-hoc target domain or task. As a result, in many real-world applications, there is a domain shift between the distributions of the train (source) and test (target) domains, causing a significant drop in performance at inference time. To tackle this problem, we propose an Unsupervised Domain Adaptation scheme for video violence detection based on single image classification that mitigates the domain gap between the two domains. We conduct experiments considering as the source labeled domain some datasets containing violent/non-violent clips in general contexts and, as the target domain, a collection of videos specific for detecting violent actions in public transport, showing that our proposed solution can improve the performance of the considered models.Source: IMPROVE 2023 - 3rd International Conference on Image Processing and Vision Engineering, pp. 37–46, Prague, Czech Republic, 21-23/04/2023
DOI: 10.5220/0011965300003497
Project(s): AI4Media
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2023 Journal article Open Access

A deep learning-based pipeline for whitefly pest abundance estimation on chromotropic sticky traps
Ciampi L., Zeni V., Incrocci L., Canale A., Benelli G., Falchi F., Amato G., Chessa S.
Integrated Pest Management (IPM) is an essential approach used in smart agriculture to manage pest populations and sustainably optimize crop production. One of the cornerstones underlying IPM solutions is pest monitoring, a practice often performed by farm owners by using chromotropic sticky traps placed on insect hot spots to gauge pest population densities. In this paper, we propose a \rev{1}{modular model-agnostic} deep learning-based counting pipeline for estimating the number of insects present in pictures of chromotropic sticky traps, thus reducing the need for manual trap inspections and minimizing human effort. Additionally, our solution generates a set of raw positions of the counted insects and confidence scores expressing their reliability, allowing practitioners to filter out unreliable predictions. We train and assess our technique by exploiting PST - Pest Sticky Traps, a new collection of dot-annotated images we created on purpose and we publicly release, suitable for counting whiteflies. Experimental evaluation shows that our proposed counting strategy can be a valuable Artificial Intelligence-based tool to help farm owners to control pest outbreaks and prevent crop damages effectively. Specifically, our solution achieves an average counting error of approximately $9\%$ compared to human capabilities requiring a matter of seconds, a large improvement respecting the time-intensive process of manual human inspections, which often take hours or even days.Source: Ecological informatics (Print) 78 (2023). doi:10.1016/j.ecoinf.2023.102384
DOI: 10.1016/j.ecoinf.2023.102384
Project(s): AI4Media
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2018 Conference article Open Access

See at: ceur-ws.org | ISTI Repository | CNR ExploRA

2019 Conference article Open Access

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
Metrics:

See at: ISTI Repository | Lecture Notes in Computer Science | link.springer.com | CNR ExploRA

2020 Journal article Open Access

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
DOI: 10.48550/arxiv.2001.03032
Project(s): AI4EU
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See at: arXiv.org e-Print Archive | Sensors | Sensors | ISTI Repository | Sensors | Sensors | doi.org | CNR ExploRA

Bus Violence: a large-scale benchmark for video violence detection in public transport
Foszner P., Staniszewski M., Szczesna A., Cogiel M., Golba D., Ciampi L., Messina N., Gennaro C., Falchi F., Amato G., Serao G.
The Bus Violence dataset is a large-scale collection of videos depicting violent and non-violent situations in public transport environments. This benchmark was gathered from multiple cameras located inside a moving bus where several people simulated violent actions, such as stealing an object from another person, fighting between passengers, etc. It contains 1,400 video clips manually annotated as having or not violent scenes, making it one of the biggest benchmarks for video violence detection in the literature.Project(s): AI4Media

See at: CNR ExploRA | zenodo.org

2022 Journal article Open Access

Bus violence: an open benchmark for video violence detection on public transport
Ciampi L., Foszner P., Messina N., Staniszewski M., Gennaro C., Falchi F., Serao G., Cogiel M., Golba D., Szczesna A., Amato G.
Automatic detection of violent actions in public places through video analysis is difficult because the employed Artificial Intelligence-based techniques often suffer from generalization problems. Indeed, these algorithms hinge on large quantities of annotated data and usually experience a drastic drop in performance when used in scenarios never seen during the supervised learning phase. In this paper, we introduce and publicly release the Bus Violence benchmark, the first large-scale collection of video clips for violence detection in public transport, where some actors simulated violent actions inside a moving bus in changing conditions such as background or light. Moreover, we conduct a performance analysis of several state-of-the-art video violence detectors pre-trained with general violence detection databases on this newly established use case. The achieved moderate performances reveal the difficulties in generalizing from these popular methods, indicating the need to have this new collection of labeled data beneficial to specialize them in this new scenario.Source: Sensors (Basel) 22 (2022). doi:10.3390/s22218345
DOI: 10.3390/s22218345
DOI: 10.3390%2fs22218345
Project(s): AI4Media
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2023 Conference article Open Access

MC-GTA: a synthetic benchmark for multi-camera vehicle tracking
Ciampi L., Messina N., Valenti G. E., Amato G., Falchi F., Gennaro C.
Multi-camera vehicle tracking (MCVT) aims to trace multiple vehicles among videos gathered from overlapping and non-overlapping city cameras. It is beneficial for city-scale traffic analysis and management as well as for security. However, developing MCVT systems is tricky, and their real-world applicability is dampened by the lack of data for training and testing computer vision deep learning-based solutions. Indeed, creating new annotated datasets is cumbersome as it requires great human effort and often has to face privacy concerns. To alleviate this problem, we introduce MC-GTA - Multi Camera Grand Tracking Auto, a synthetic collection of images gathered from the virtual world provided by the highly-realistic Grand Theft Auto 5 (GTA) video game. Our dataset has been recorded from several cameras recording urban scenes at various crossroads. The annotations, consisting of bounding boxes localizing the vehicles with associated unique IDs consistent across the video sources, have been automatically generated by interacting with the game engine. To assess this simulated scenario, we conduct a performance evaluation using an MCVT SOTA approach, showing that it can be a valuable benchmark that mitigates the need for real-world data. The MC-GTA dataset and the code for creating new ad-hoc custom scenarios are available at https://github.com/GaetanoV10/GT5-Vehicle-BB.Source: ICIAP 2023 - 22nd International Conference on Image Analysis and Processing, pp. 316–327, Udine, Italy, 11-15/09/2023
DOI: 10.1007/978-3-031-43148-7_27
Project(s): AI4Media
Metrics:

See at: ISTI Repository | link.springer.com | CNR ExploRA

2019 Conference article Open Access

See at: ISTI Repository | www.ital-ia.it | CNR ExploRA