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2023 Italia Intelligenza Artificiale - Thematic Workshops, Ital-IA 2023

作者： Farinella, Giovanni Maria Furnari, Antonino FPV@IPLAB Group Department of Mathematics and Computer Science University of Catania Italy

Integrating artificial intelligence and computer vision on wearable devices in industrial environments can increase productivity, efficiency, and safety in the workplace. Despite the availability of wearable devices such as Microsoft HoloLens, Magic Leap, and nreal, the application of artificial intelligence algorithms on wearable devices equipped with cameras is an open research topic. To address this gap, the fpv@iplab group at the University of Catania has conducted research on the construction of machine learning and computer vision algorithms for portable devices. The research has focused on three main areas: localization and navigation, user-object interaction understanding, and user-object interaction anticipation. The work conducted by the fpv@iplab group aims to enhance the use wearable devices and to develop artificial intelligence techniques that can improve workplace efficiency and safety. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： computer vision

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Exploiting Multimodal Synthetic Data for Egocentric Human-Object Interaction Detection in an Industrial Scenario

arXiv

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arXiv 2023年

作者： Leonardi, Rosario Ragusa, Francesco Furnari, Antonino Farinella, Giovanni Maria FPV@IPLAB Department of Mathematics and Computer Science University of Catania Catania95125 Italy Next Vision s.r.l Spinoff of the University of Catania Viale Andrea Doria 6 Catania95125 Italy

In this paper, we tackle the problem of Egocentric Human-Object Interaction (EHOI) detection in an industrial setting. To overcome the lack of public datasets in this context, we propose a pipeline and a tool for generating synthetic images of EHOIs paired with several annotations and data signals (e.g., depth maps or segmentation masks). Using the proposed pipeline, we present EgoISM-HOI a new multimodal dataset composed of synthetic EHOI images in an industrial environment with rich annotations of hands and objects. To demonstrate the utility and effectiveness of synthetic EHOI data produced by the proposed tool, we designed a new method that predicts and combines different multimodal signals to detect EHOIs in RGB images. Our study shows that exploiting synthetic data to pre-train the proposed method significantly improves performance when tested on real-world data. Moreover, to fully understand the usefulness of our method, we conducted an in-depth analysis in which we compared and highlighted the superiority of the proposed approach over different state-of-the-art class-agnostic methods. To support research in this field, we publicly release the datasets, source code, and pre-trained models at https://***/egoism-hoi. © 2023, CC BY.

关键词： Pipelines

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Image-based Navigation in Real-World Environments via Multiple Mid-level Representations: Fusion Models, Benchmark and Efficient Evaluation

arXiv

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arXiv 2022年

作者： Rosano, Marco Furnari, Antonino Gulino, Luigi Santoro, Corrado Farinella, Giovanni Maria FPV@IPLAB - Department of Mathematics and Computer Science University of Catania Catania Italy Robotics Laboratory Department of Mathematics and Computer Science University of Catania Catania Italy OrangeDev s.r.l. Firenze Italy Cognitive Robotics and Social Sensing Laboratory ICAR-CNR Palermo Italy Next Vision s.r.l. Catania Italy

Robot visual navigation is a relevant research topic. Current deep navigation models conveniently learn the navigation policies in simulation, given the large amount of experience they need to collect. Unfortunately, the resulting models show a limited generalization ability when deployed in the real world. In this work we explore solutions to facilitate the development of visual navigation policies trained in simulation that can be successfully transferred in the real world. We first propose an efficient evaluation tool to reproduce realistic navigation episodes in simulation. We then investigate a variety of deep fusion architectures to combine a set of mid-level representations, with the aim of finding the best merge strategy that maximize the real world performances. Our experiments, performed both in simulation and on a robotic platform, show the effectiveness of the considered mid-level representations-based models and confirm the reliability of the evaluation tool. The 3D models of the environment and the code of the validation tool are publicly available at the following link: https://***/EmbodiedVN/ © 2022, CC BY-NC-SA.

关键词： Reinforcement learning

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Image-Based Navigation in Real-World Environments Via Multiple Mid-Level Representations: Fusion Models, Benchmark and Efficient Evaluation

SSRN

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SSRN 2022年

作者： Rosano, Marco Furnari, Antonino Gulino, Luigi Santoro, Corrado Farinella, Giovanni Maria FPV@IPLAB Department of Mathematics and Computer Science University of Catania Catania Italy Robotics Laboratory Department of Mathematics and Computer Science University of Catania Catania Italy OrangeDev s.r.l. Firenze Italy Cognitive Robotics and Social Sensing Laboratory ICAR-CNR Palermo Italy Next Vision s.r.l. Catania Italy

Robot visual navigation is a relevant research topic. Current deep navigation models mostly learn the navigation policies in simulation. This is convenient, given the efficiency offered by simulators to collect the required training experience. Unfortunately, the resulting models show a limited generalization ability when deployed in the real world. In this work we investigate the problem of learning robust visual navigation policies in simulation that can successfully transferred in the real world. We propose an evaluation tool to reproduce realistic navigation episodes in simulation. We then study whether the mid-level visual representations, that have proven to be effective in previous applications, can be successfully used in simulation to solve the domain gap. Our experiments, performed both in simulation and on a robotic platform, show the proposed evaluation tool can provide results which are consistent with the ones obtained in the real world. © 2022, The Authors. All rights reserved.

关键词： Reinforcement learning

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On embodied visual navigation in real environments through Habitat

arXiv

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arXiv 2020年

作者： Rosano, Marco Furnari, Antonino Gulino, Luigi Farinella, Giovanni Maria FPV@IPLAB Department of Mathematics and Computer Science University of Catania Italy Cognitive Robotics and Social Sensing Laboratory ICAR-CNR Palermo Italy OrangeDev s.r.l. Firenze Italy

Visual navigation models based on deep learning can learn effective policies when trained on large amounts of visual observations through reinforcement learning. Unfortunately, collecting the required experience in the real world requires the deployment of a robotic platform, which is expensive and time-consuming. To deal with this limitation, several simulation platforms have been proposed in order to train visual navigation policies on virtual environments efficiently. Despite the advantages they offer, simulators present a limited realism in terms of appearance and physical dynamics, leading to navigation policies that do not generalize in the real world. In this paper, we propose a tool based on the Habitat simulator which exploits real world images of the environment, together with sensor and actuator noise models, to produce more realistic navigation episodes. We perform a range of experiments to assess the ability of such policies to generalize using virtual and real-world images, as well as observations transformed with unsupervised domain adaptation approaches. We also assess the impact of sensor and actuation noise on the navigation performance and investigate whether it allows to learn more robust navigation policies. We show that our tool can effectively help to train and evaluate navigation policies on real-world observations without running navigation episodes in the real world. Copyright © 2020, The Authors. All rights reserved.

关键词： Ecosystems

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Selecting discriminative CLBP patterns for age estimation

Selecting discriminative CLBP patterns for age estimation

引用

IEEE International Conference on Multimedia and Expo Workshops (ICMEW)

作者： Alessandro Torrisi Giovanni Maria Farinella Giovanni Puglisi Sebastiano Battiato IPLAB University of Catania Department of Mathematics and Computer Science University of Cagliari

Face analysis is of great interest in the context of digital signage to understand soft biometric of a person. Among the others information gathered from a face, the age of a person is still an open challenging problem. Face representation takes an important role for real time age discrimination. LBP descriptor and the related variants (e.g., CLBP) have been demonstrated to obtain the state-of-the-art performances in this field. In this paper, building on the CLBP representation, we propose a method to select the most discriminative CLBP patterns to represent faces for age classification. Experiments confirm that the proposed method improves the age classification accuracy by reducing computational costs both in terms of space and time.

关键词： Estimation Face Accuracy Aging Training Histograms Active appearance model

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