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University of Catania, Department of Mathematics and Computer Science, Via Santa Sofia - 64, Catania 95125, Italy, University of Catania, CUTGANA, Viale A. Doria 6, Catania 95123 and Italy

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14 Papers

Calisthenics Skills Temporal Video Segmentation

Topic: Event and Human Activity Recognition

Authors: Antonio Finocchiaro , Giovanni Farinella and Antonino Furnari

Affiliation: Department of Mathematics and Computer Science, University of Catania, Italy

Digital Object Identifier: 10.5220/0012400600003660

Proceedings of VISAPP 2024, ISBN: 978-989-758-679-8

Quick Abstract

Calisthenics is a fast-growing bodyweight discipline that consists of different categories, one of which is focused on skills. Skills in calisthenics encompass both static and dynamic elements performed by athletes. The evaluation of static skills is based on their difficulty level and the duration of the hold. Automated tools able to recognize isometric skills from a video by segmenting them to estimate their duration would be desirable to assist athletes in their training and judges during competitions. Although the video understanding literature on action recognition through body pose analysis is rich, no previous work has specifically addressed the problem of calisthenics skill temporal video segmentation. This study aims to provide an initial step towards the implementation of automated tools within the field of Calisthenics. To advance knowledge in this context, we propose a dataset of video footage of static calisthenics skills performed by athletes. Each video is annotated with a temporal segmentation which determines the extent of each skill. We hence report the results of a baseline approach to address the problem of skill temporal segmentation on the proposed dataset. The results highlight the feasibility of the proposed problem, while there is still room for improvement.

HERO-GPT: Zero-Shot Conversational Assistance in Industrial Domains Exploiting Large Language Models

Topics: Interactive Visual Systems; Virtual and Augmented Reality Applications

Authors: Luca Strano ¹ , Claudia Bonanno ¹ , Francesco Ragusa ^{2

;

1} , Giovanni Farinella ^{3

;

2

;

1} and Antonino Furnari ^{1

;

2}

Affiliations: ¹ FPV@IPLAB, DMI - University of Catania, Italy , ² Next Vision s.r.l. - Spinoff of the University of Catania, Italy , ³ Cognitive Robotics and Social Sensing Laboratory, ICAR-CNR, Palermo, Italy

Digital Object Identifier: 10.5220/0012688900003720

Proceedings of IMPROVE 2024, ISBN: 978-989-758-693-4

Quick Abstract

We introduce HERO-GPT, a Multi-Modal Virtual Assistant built on a Multi-Agent System designed to swiftly adapt to any procedural context minimizing the need for training on context-specific data. In contrast to traditional approaches to conversational agents, HERO-GPT utilizes a series of dynamically interchangeable documents instead of datasets, hand-written rules, or conversational examples, to provide information on the given scenario. This paper presents the system’s capability to adapt to an industrial domain scenario through the integration of a GPT-based Large Language Model and an object detector to support Visual Question Answering. HERO-GPT is capable of offering conversational guidance on various aspects of industrial contexts, including information on Personal Protective Equipment (PPE), machinery, procedures, and best practices. Experiments performed in an industrial laboratory with real users demonstrate HERO-GPT’s effectiveness. Results indicate that users clearly prefer the proposed virtual assistant over traditional supporting materials such as paper-based manuals in the considered scenario. Moreover, the performance of the proposed system are shown to be comparable or superior to those of traditional approaches, while requiring little domain-specific data for the setup of the system.

ENIGMA: Egocentric Navigator for Industrial Guidance, Monitoring and Anticipation

Topics: Mobile and Egocentric Object Detection and Recognition; Mobile and Egocentric Localization; Egocentric Vision for Interaction Understanding

Authors: Francesco Ragusa ^{1

;

2} , Antonino Furnari ^{2

;

1} , Antonino Lopes ³ , Marco Moltisanti ³ , Emanuele Ragusa ³ , Marina Samarotto ³ , Luciano Santo ³ , Nicola Picone ⁴ , Leo Scarso ⁴ and Giovanni Farinella ^{1

;

2}

Affiliations: ¹ Next Vision s.r.l. - Spinoff of the University of Catania, Italy , ² FPV@IPLAB, DMI - University of Catania, Italy , ³ Xenia Gestione Documentale s.r.l. - Xenia Progetti s.r.l., Acicastello, Catania, Italy , ⁴ Morpheos s.r.l. - Catania, Italy

Digital Object Identifier: 10.5220/0011787900003417

Proceedings of VISAPP 2023/VISIGRAPP, ISBN: 978-989-758-634-7

Quick Abstract

We present ENIGMA (Egocentric Navigator for Industrial Guidance, Monitoring and Anticipation), an integrated system to support workers in an industrial laboratory. ENIGMA includes a wearable assistant which understands the worker’s behavior through Computer Vision algorithms which 1) localize the operator, 2) recognize the objects present in the laboratory, 3) detect the human-object interactions which happen and 4) anticipate the next-active object with which the worker will interact. Furthermore, a back-end extracts high semantic information about the worker behavior to provide useful services and to improve his safety. Preliminary experiments were conducted showing good performance on the tasks of localization, object detection and recognition and egocentric human-object interaction detection considering the challenging industrial scenario.

HERO: An Artificial Conversational Assistant to Support Humans in Industrial Scenarios

Topic: Multidimensional Signal Processing

Authors: Claudia Bonanno ¹ , Francesco Ragusa ^{1

;

2} , Rosario Leonardi ¹ , Antonino Furnari ^{1

;

2} and Giovanni Maria Farinella ^{1

;

2}

Affiliations: ¹ FPV@IPLAB, DMI - University of Catania, Italy , ² Next Vision s.r.l. - Spinoff of the University of Catania, Italy

Digital Object Identifier: 10.5220/0011318600003289

Proceedings of SIGMAP 2022, ISBN: 978-989-758-591-3

Quick Abstract

We present HERO, a Conversational Intelligent Assistant to support workers in industrial domains. The proposed system is able to interact with humans using natural language and observing the surrounding world in order to avoid the language ambiguity. HERO is composed of four modules: 1) the input module to process both text and visual signals, 2) the NLP module to predict user intent and extract relevant entities from text, 3) the object detection module to extract entities by analyzing images captured by the user and 4) the output module which is responsible for choosing the best answer to send to the user. To assess its usefulness in a real scenario, the proposed system is implemented and evaluated in an industrial laboratory setting. Preliminary experiments show that HERO achieves good performance in predicting intents and entities exploiting both text and visual signals.

Tiny Neural Network Pipeline for Vocal Commands Recognition @Edge

Topics: Agent-based Human-Computer-Interaction; Mobile, Wearable and Pervasive Devices; Input and Output Modalities; User Modeling and Profiling

Authors: Ivana Guarneri ¹ , Alessandro Lauria ² , Giovanni Maria Farinella ² and Corrado Santoro ²

Affiliations: ¹ STMicroelectronics, System Research and Applications, Stradale Primosole 50, Catania, Italy , ² Dipartimento di Matematica e Informatica, Università degli Studi di Catania, Catania, Italy

Digital Object Identifier: 10.5220/0010908800003124

Proceedings of HUCAPP 2022/VISIGRAPP, ISBN: 978-989-758-555-5

Quick Abstract

Vocal analysis and speech recognition have been a challenge for the research community for a long time. The widespread of deep learning approaches, the availability of big datasets and the increasing computational capabilities of processors, have contributed to achieve disruptive results in this field. Most of the high performing existing speech recognition systems are available as cloud services. Other systems are hybrid, with some parts on the cloud and some modules running on the microcontroller. One of the challenges is to realize high performing speech recognition systems running on the edge, where the edge is an integrated platform, composed by a processing unit, a bank of memory and a power unit. In this paper is proposed an end-to-end deep learning approach to recognize a set of vocal commands able to work on an edge IoT node. Tests have been performed on a tiny platform and the study with different settings is reported.

Visual RSSI Fingerprinting for Radio-based Indoor Localization

Topics: Multidimensional Signal Processing; Sensors and Multimedia

Authors: Giuseppe Puglisi ¹ , Daniele Di Mauro ¹ , Antonino Furnari ¹ , Luigi Gulino ² and Giovanni M. Farinella ¹

Affiliations: ¹ Department of Mathematics and Computer Science, University of Catania, Catania, Italy , ² Orangedev S.r.l., Italy

Digital Object Identifier: 10.5220/0011299900003289

Proceedings of SIGMAP 2022, ISBN: 978-989-758-591-3

Quick Abstract

The problem of localizing objects exploiting RSSI signals has been tackled using both geometric and machine learning based methods. Solutions machine learning based have the advantage to better cope with noise, but require many radio signal observations associated to the correct position in the target space. This data collection and labeling process is not trivial and it typically requires building a grid of dense observations, which can be resource-intensive. To overcome this issue, we propose a pipeline which uses an autonomous robot to collect RSSI-image pairs and Structure from Motion to associate 2D positions to the RSSI values based on the inferred position of each image. This method, as we shown in the paper, allows to acquire large quantities of data in an inexpensive way. Using the collected data, we experiment with machine learning models based on RNNs and propose an optimized model composed of a set of LSTMs that specialize on the RSSI observations coming from different antennas. The proposed method shows promising results outperforming different baselines, suggesting that the proposed pipeline allowing to collect and automatically label observations is useful in real scenarios. Furthermore, to aid research in this area, we publicly release the collected dataset comprising 57158 RSSI observations paired with RGB images.

Unsupervised Domain Adaptation for 6DOF Indoor Localization

Topics: Imaging and Vision for Cultural Heritage; Mobile and Egocentric Localization; First Person Vision; Domain Adaptation

Authors: Daniele Di Mauro ¹ , Antonino Furnari ¹ , Giovanni Maria Signorello ² and Giovanni Maria Farinella ¹

Affiliations: ¹ Department of Mathematics and Computer Science, University of Catania, Piazza Università 2, Catania, Italy , ² CUTGANA, University of Catania, Piazza Università 2, Catania, Italy

Digital Object Identifier: 10.5220/0010333409540961

Proceedings of VISAPP 2021/VISIGRAPP, ISBN: 978-989-758-488-6

Quick Abstract

Visual Localization is gathering more and more attention in computer vision due to the spread of wearable cameras (e.g. smart glasses) and to the increase of general interest in autonomous vehicles and robots. Unfortunately, current localization algorithms rely on large amounts of labeled training data collected in the specific target environment in which the system needs to work. Data collection and labeling in this context is difficult and time-consuming. Moreover, the process has to be repeated when the system is adapted to a new environment. In this work, we consider a scenario in which the target environment has been scanned to obtain a 3D model of the scene suitable to generate large quantities of synthetic data automatically paired with localization labels. We hence investigate the use of Unsupervised Domain Adaptation techniques exploiting labeled synthetic data and unlabeled real data to train localization algorithms. To carry out the study, we introduce a new dataset composed of synthetic and real images labeled with their 6-DOF poses collected in four different indoor rooms which is available at https://iplab.dmi.unict.it/EGO-CH-LOC-UDA. A new method based on self-supervision and attention modules is hence proposed and tested on the proposed dataset. Results show that our method improves over baselines and state-of-the-art algorithms tackling similar domain adaptation tasks.

A Comparison of Visual Navigation Approaches based on Localization and Reinforcement Learning in Virtual and Real Environments

Topics: Vision for Robotics; Egocentric Vision for Learning to Act; Mobile and Egocentric Localization; First Person Vision; Active Vision

Authors: Marco Rosano ^{1

;

2} , Antonino Furnari ² , Luigi Gulino ¹ and Giovanni Maria Farinella ²

Affiliations: ¹ OrangeDev s.r.l., via Vasco de Gama 91, Firenze, Italy , ² Università degli Studi di Catania, Catania, Italy

Digital Object Identifier: 10.5220/0008950806280635

Proceedings of VISAPP 2020/VISIGRAPP, ISBN: 978-989-758-402-2

Quick Abstract

Visual navigation algorithms allow a mobile agent to sense the environment and autonomously find its way to reach a target (e.g. an object in the environment). While many recent approaches tackled this task using reinforcement learning, which neglects any prior knowledge about the environments, more classic approaches strongly rely on self-localization and path planning. In this study, we compare the performance of single-target and multi-target visual navigation approaches based on the reinforcement learning paradigm, and simple baselines which rely on image-based localization. Experiments performed on discrete-state environments of different sizes, comprised of both real and virtual images, show that the two paradigms tend to achieve complementary results, hence suggesting that a combination of the two approaches to visual navigation may be beneficial.

Localizing Visitors in Natural Sites Exploiting Modality Attention on Egocentric Images and GPS Data

Topics: Machine Learning Technologies for Vision; Imaging and Vision for Cultural Heritage; Understanding from Wearable and Mobile Cameras; Mobile and Egocentric Behavioural Analysis

Authors: Giovanni Maria Pasqualino ¹ , Stefano Scafiti ¹ , Antonino Furnari ¹ and Giovanni Maria Farinella ^{2

;

1}

Affiliations: ¹ Department of Mathematics and Computer Science, University of Catania, Catania, Italy , ² Cognitive Robotics and Social Sensing Laboratory, ICAR-CNR, Palermo, Italy

Digital Object Identifier: 10.5220/0009103906090617

Proceedings of VISAPP 2020/VISIGRAPP, ISBN: 978-989-758-402-2

Quick Abstract

Localizing the visitors of an outdoor natural site can be advantageous to study their behavior as well as to provide them information on where they are and what to visit in the site. Despite GPS can generally be used to perform outdoor localization, we show that this kind of signal is not always accurate enough in real-case scenarios. On the contrary, localization based on egocentric images can be more accurate but it generally results in more expensive computation. In this paper, we investigate how fusing image- and GPS-based predictions can allow to achieve efficient and accurate localization of the visitors of a natural site. Specifically, we compare different fusion techniques, including a modality attention approach which is shown to provide the best performances. Results point out that the proposed technique achieve promising results, allowing to obtain the performances of very deep models (e.g., DenseNet) with a less expensive architecture (e.g., SqueezeNet) which employ a memory footprint of about 3MB and an inference speed of about 25ms.

An Overview on Image Sentiment Analysis: Methods, Datasets and Current Challenges

Topics: Semantic Analysis of Multimedia Data; Social Network Analysis; Semantic Aware Multimedia Indexing (E.G., Affect, Sentiment, Interest); Crowdsourced Multimedia Analysis and Applications; Emotional and Social Signals in Multimedia ; Collecting Large Scale Social Signal Data; Emotional Multimedia Content Tagging

Authors: Alessandro Ortis , Giovanni Maria Farinella and Sebastiano Battiato

Affiliation: University of Catania, Department of Mathematics and Computer Science, Viale A. Doria, 6, Catania, 95125 and Italy

Digital Object Identifier: 10.5220/0007909602900300

Proceedings of SIGMAP 2019, ISBN: 978-989-758-378-0

Quick Abstract

This paper introduces the research field of Image Sentiment Analysis, analyses the related problems, provides an in-depth overview of current research progress, discusses the major issues and outlines the new opportunities and challenges in this area. An overview of the most significant works is presented. A discussion about the related specific issues is provided: emotions representation models, existing datasets and most used features. A generalizable analysis of the problem is also presented, by identifying and analyzing the components that affect the sentiment toward an image. Furthermore, the paper introduces some additional challenges and techniques that could be investigated, proposing suggestions for new methods, features and datasets.

Related Ontology Subjects/Areas/Topics (4)