2023 Conference article Open Access

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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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See at: ISTI Repository | CNR ExploRA | www.scitepress.org

2022 Journal article Open Access

Comparing the performance of Hebbian against backpropagation learning using convolutional neural networks
Lagani G., Falchi F., Gennaro C., Amato G.
In this paper, we investigate Hebbian learning strategies applied to Convolutional Neural Network (CNN) training. We consider two unsupervised learning approaches, Hebbian Winner-Takes-All (HWTA), and Hebbian Principal Component Analysis (HPCA). The Hebbian learning rules are used to train the layers of a CNN in order to extract features that are then used for classification, without requiring backpropagation (backprop). Experimental comparisons are made with state-of-the-art unsupervised (but backprop-based) Variational Auto-Encoder (VAE) training. For completeness,we consider two supervised Hebbian learning variants (Supervised Hebbian Classifiers--SHC, and Contrastive Hebbian Learning--CHL), for training the final classification layer, which are compared to Stochastic Gradient Descent training. We also investigate hybrid learning methodologies, where some network layers are trained following the Hebbian approach, and others are trained by backprop. We tested our approaches on MNIST, CIFAR10, and CIFAR100 datasets. Our results suggest that Hebbian learning is generally suitable for training early feature extraction layers, or to retrain higher network layers in fewer training epochs than backprop. Moreover, our experiments show that Hebbian learning outperforms VAE training, with HPCA performing generally better than HWTA.Source: Neural computing & applications (Print) (2022). doi:10.1007/s00521-021-06701-4
DOI: 10.1007/s00521-021-06701-4
Project(s): AI4EU , AI4Media
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2022 Conference article Open Access

See at: ISTI Repository | CNR ExploRA | www.ital-ia2022.it

2022 Conference article Open Access

See at: ISTI Repository | CNR ExploRA | www.ital-ia2022.it

2022 Conference article Open Access

See at: ISTI Repository | CNR ExploRA | www.ital-ia2022.it

2022 Conference article Open Access

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2022 Conference article Open Access

See at: ISTI Repository | CNR ExploRA | www.ital-ia2022.it

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 Contribution to book Open Access

Training convolutional neural networks with competitive hebbian learning approaches
Lagani G., Falchi F., Gennaro C., Amato G.
We explore competitive Hebbian learning strategies to train feature detectors in Convolutional Neural Networks (CNNs), without supervision. We consider variants of the Winner-Takes-All (WTA) strategy explored in previous works, i.e. k-WTA, e-soft-WTA and p-soft-WTA, performing experiments on different object recognition datasets. Results suggest that the Hebbian approaches are effective to train early feature extraction layers, or to re-train higher layers of a pre-trained network, with soft competition generally performing better than other Hebbian approaches explored in this work. Our findings encourage a path of cooperation between neuroscience and computer science towards a deeper investigation of biologically inspired learning principles.Source: Machine Learning, Optimization, and Data Science, edited by Nicosia G., Ojha V., La Malfa E., La Malfa G., Jansen G., Pardalos P.M., Giuffrida G., Umeton R., pp. 25–40, 2022
DOI: 10.1007/978-3-030-95467-3_2
Project(s): AI4EU , AI4Media
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2022 Contribution to book Open Access

Evaluating hebbian learning in a semi-supervised setting
Lagani G., Falchi F., Gennaro C., Amato G.
We propose a semi-supervised learning strategy for deep Convolutional Neural Networks (CNNs) in which an unsupervised pre-training stage, performed using biologically inspired Hebbian learning algorithms, is followed by supervised end-to-end backprop fine-tuning. We explored two Hebbian learning rules for the unsupervised pre-training stage: soft-Winner-Takes-All (soft-WTA) and nonlinear Hebbian Principal Component Analysis (HPCA). Our approach was applied in sample efficiency scenarios, where the amount of available labeled training samples is very limited, and unsupervised pre-training is therefore beneficial. We performed experiments on CIFAR10, CIFAR100, and Tiny ImageNet datasets. Our results show that Hebbian outperforms Variational Auto-Encoder (VAE) pre-training in almost all the cases, with HPCA generally performing better than soft-WTA.Source: Machine Learning, Optimization, and Data Science, edited by Nicosia G.; Ojha V.; La Malfa E.; La Malfa G.; Jansen G.; Pardalos P.M.; Giuffrida G.; Umeton R., pp. 365–379, 2022
DOI: 10.1007/978-3-030-95470-3_28
Project(s): AI4EU , AI4Media
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2022 Journal article Open Access

An embedded toolset for human activity monitoring in critical environments
Di Benedetto M., Carrara F., Ciampi L., Falchi F., Gennaro C., Amato G.
In many working and recreational activities, there are scenarios where both individual and collective safety have to be constantly checked and properly signaled, as occurring in dangerous workplaces or during pandemic events like the recent COVID-19 disease. From wearing personal protective equipment to filling physical spaces with an adequate number of people, it is clear that a possibly automatic solution would help to check compliance with the established rules. Based on an off-the-shelf compact and low-cost hardware, we present a deployed real use-case embedded system capable of perceiving people's behavior and aggregations and supervising the appliance of a set of rules relying on a configurable plug-in framework. Working on indoor and outdoor environments, we show that our implementation of counting people aggregations, measuring their reciprocal physical distances, and checking the proper usage of protective equipment is an effective yet open framework for monitoring human activities in critical conditions.Source: Expert systems with applications 199 (2022). doi:10.1016/j.eswa.2022.117125
DOI: 10.1016/j.eswa.2022.117125
Project(s): AI4EU , 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

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2022 Conference article Open Access

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2022 Dataset Open Access

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2022 Journal article Open Access

Learning to count biological structures with raters' uncertainty
Ciampi L., Carrara F., Totaro V., Mazziotti R., Lupori L., Santiago C., Amato G., Pizzorusso T., Gennaro C.
Exploiting well-labeled training sets has led deep learning models to astonishing results for counting biological structures in microscopy images. However, dealing with weak multi-rater annotations, i.e., when multiple human raters disagree due to non-trivial patterns, remains a relatively unexplored problem. More reliable labels can be obtained by aggregating and averaging the decisions given by several raters to the same data. Still, the scale of the counting task and the limited budget for labeling prohibit this. As a result, making the most with small quantities of multi-rater data is crucial. To this end, we propose a two-stage counting strategy in a weakly labeled data scenario. First, we detect and count the biological structures; then, in the second step, we refine the predictions, increasing the correlation between the scores assigned to the samples and the raters' agreement on the annotations. We assess our methodology on a novel dataset comprising fluorescence microscopy images of mice brains containing extracellular matrix aggregates named perineuronal nets. We demonstrate that we significantly enhance counting performance, improving confidence calibration by taking advantage of the redundant information characterizing the small sets of available multi-rater data.Source: Medical image analysis (Print) 80 (2022). doi:10.1016/j.media.2022.102500
DOI: 10.1016/j.media.2022.102500
Project(s): AI4Media
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2022 Conference article Open Access

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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
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See at: ISTI Repository | CNR ExploRA | www.sciencedirect.com