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Author Silvio Giancola; Anthony Cioppa; Adrien Deliege; Floriane Magera; Vladimir Somers; Le Kang; Xin Zhou; Olivier Barnich; Christophe De Vleeschouwer; Alexandre Alahi; Bernard Ghanem; Marc Van Droogenbroeck; Abdulrahman Darwish; Adrien Maglo; Albert Clapes; Andreas Luyts; Andrei Boiarov; Artur Xarles; Astrid Orcesi; Avijit Shah; Baoyu Fan; Bharath Comandur; Chen Chen; Chen Zhang; Chen Zhao; Chengzhi Lin; Cheuk-Yiu Chan; Chun Chuen Hui; Dengjie Li; Fan Yang; Fan Liang; Fang Da; Feng Yan; Fufu Yu; Guanshuo Wang; H. Anthony Chan; He Zhu; Hongwei Kan; Jiaming Chu; Jianming Hu; Jianyang Gu; Jin Chen; Joao V. B. Soares; Jonas Theiner; Jorge De Corte; Jose Henrique Brito; Jun Zhang; Junjie Li; Junwei Liang; Leqi Shen; Lin Ma; Lingchi Chen; Miguel Santos Marques; Mike Azatov; Nikita Kasatkin; Ning Wang; Qiong Jia; Quoc Cuong Pham; Ralph Ewerth; Ran Song; Rengang Li; Rikke Gade; Ruben Debien; Runze Zhang; Sangrok Lee; Sergio Escalera; Shan Jiang; Shigeyuki Odashima; Shimin Chen; Shoichi Masui; Shouhong Ding; Sin-wai Chan; Siyu Chen; Tallal El-Shabrawy; Tao He; Thomas B. Moeslund; Wan-Chi Siu; Wei Zhang; Wei Li; Xiangwei Wang; Xiao Tan; Xiaochuan Li; Xiaolin Wei; Xiaoqing Ye; Xing Liu; Xinying Wang; Yandong Guo; Yaqian Zhao; Yi Yu; Yingying Li; Yue He; Yujie Zhong; Zhenhua Guo; Zhiheng Li
Title SoccerNet 2022 Challenges Results Type (up) Conference Article
Year 2022 Publication 5th International ACM Workshop on Multimedia Content Analysis in Sports Abbreviated Journal
Volume Issue Pages 75-86
Keywords
Abstract The SoccerNet 2022 challenges were the second annual video understanding challenges organized by the SoccerNet team. In 2022, the challenges were composed of 6 vision-based tasks: (1) action spotting, focusing on retrieving action timestamps in long untrimmed videos, (2) replay grounding, focusing on retrieving the live moment of an action shown in a replay, (3) pitch localization, focusing on detecting line and goal part elements, (4) camera calibration, dedicated to retrieving the intrinsic and extrinsic camera parameters, (5) player re-identification, focusing on retrieving the same players across multiple views, and (6) multiple object tracking, focusing on tracking players and the ball through unedited video streams. Compared to last year's challenges, tasks (1-2) had their evaluation metrics redefined to consider tighter temporal accuracies, and tasks (3-6) were novel, including their underlying data and annotations. More information on the tasks, challenges and leaderboards are available on this https URL. Baselines and development kits are available on this https URL.
Address Lisboa; Portugal; October 2022
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference ACMW
Notes HUPBA; no menciona Approved no
Call Number Admin @ si @ GCD2022 Serial 3801
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Author Dustin Carrion Ojeda; Hong Chen; Adrian El Baz; Sergio Escalera; Chaoyu Guan; Isabelle Guyon; Ihsan Ullah; Xin Wang; Wenwu Zhu
Title NeurIPS’22 Cross-Domain MetaDL competition: Design and baseline results Type (up) Conference Article
Year 2022 Publication Understanding Social Behavior in Dyadic and Small Group Interactions Abbreviated Journal
Volume 191 Issue Pages 24-37
Keywords
Abstract We present the design and baseline results for a new challenge in the ChaLearn meta-learning series, accepted at NeurIPS'22, focusing on “cross-domain” meta-learning. Meta-learning aims to leverage experience gained from previous tasks to solve new tasks efficiently (i.e., with better performance, little training data, and/or modest computational resources). While previous challenges in the series focused on within-domain few-shot learning problems, with the aim of learning efficiently N-way k-shot tasks (i.e., N class classification problems with k training examples), this competition challenges the participants to solve “any-way” and “any-shot” problems drawn from various domains (healthcare, ecology, biology, manufacturing, and others), chosen for their humanitarian and societal impact. To that end, we created Meta-Album, a meta-dataset of 40 image classification datasets from 10 domains, from which we carve out tasks with any number of “ways” (within the range 2-20) and any number of “shots” (within the range 1-20). The competition is with code submission, fully blind-tested on the CodaLab challenge platform. The code of the winners will be open-sourced, enabling the deployment of automated machine learning solutions for few-shot image classification across several domains.
Address
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Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference PMLR
Notes HUPBA; no menciona Approved no
Call Number Admin @ si @ CCB2022 Serial 3802
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Author Patricia Suarez; Dario Carpio; Angel Sappa; Henry Velesaca
Title Transformer based Image Dehazing Type (up) Conference Article
Year 2022 Publication 16th IEEE International Conference on Signal Image Technology & Internet Based System Abbreviated Journal
Volume Issue Pages
Keywords atmospheric light; brightness component; computational cost; dehazing quality; haze-free image
Abstract This paper presents a novel approach to remove non homogeneous haze from real images. The proposed method consists mainly of image feature extraction, haze removal, and image reconstruction. To accomplish this challenging task, we propose an architecture based on transformers, which have been recently introduced and have shown great potential in different computer vision tasks. Our model is based on the SwinIR an image restoration architecture based on a transformer, but by modifying the deep feature extraction module, the depth level of the model, and by applying a combined loss function that improves styling and adapts the model for the non-homogeneous haze removal present in images. The obtained results prove to be superior to those obtained by state-of-the-art models.
Address Dijon; France; October 2022
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference SITIS
Notes MSIAU; no proj Approved no
Call Number Admin @ si @ SCS2022 Serial 3803
Permanent link to this record
 
 
Author Angel Sappa; Patricia Suarez; Henry Velesaca; Dario Carpio
Title Domain Adaptation in Image Dehazing: Exploring the Usage of Images from Virtual Scenarios Type (up) Conference Article
Year 2022 Publication 16th International Conference on Computer Graphics, Visualization, Computer Vision and Image Processing Abbreviated Journal
Volume Issue Pages 85-92
Keywords Domain adaptation; Synthetic hazed dataset; Dehazing
Abstract This work presents a novel domain adaptation strategy for deep learning-based approaches to solve the image dehazing
problem. Firstly, a large set of synthetic images is generated by using a realistic 3D graphic simulator; these synthetic
images contain different densities of haze, which are used for training the model that is later adapted to any real scenario.
The adaptation process requires just a few images to fine-tune the model parameters. The proposed strategy allows
overcoming the limitation of training a given model with few images. In other words, the proposed strategy implements
the adaptation of a haze removal model trained with synthetic images to real scenarios. It should be noticed that it is quite
difficult, if not impossible, to have large sets of pairs of real-world images (with and without haze) to train in a supervised
way dehazing algorithms. Experimental results are provided showing the validity of the proposed domain adaptation
strategy.
Address Lisboa; Portugal; July 2022
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference CGVCVIP
Notes MSIAU; no proj Approved no
Call Number Admin @ si @ SSV2022 Serial 3804
Permanent link to this record
 
 
Author Alex Falcon; Swathikiran Sudhakaran; Giuseppe Serra; Sergio Escalera; Oswald Lanz
Title Relevance-based Margin for Contrastively-trained Video Retrieval Models Type (up) Conference Article
Year 2022 Publication ICMR '22: Proceedings of the 2022 International Conference on Multimedia Retrieval Abbreviated Journal
Volume Issue Pages 146-157
Keywords
Abstract Video retrieval using natural language queries has attracted increasing interest due to its relevance in real-world applications, from intelligent access in private media galleries to web-scale video search. Learning the cross-similarity of video and text in a joint embedding space is the dominant approach. To do so, a contrastive loss is usually employed because it organizes the embedding space by putting similar items close and dissimilar items far. This framework leads to competitive recall rates, as they solely focus on the rank of the groundtruth items. Yet, assessing the quality of the ranking list is of utmost importance when considering intelligent retrieval systems, since multiple items may share similar semantics, hence a high relevance. Moreover, the aforementioned framework uses a fixed margin to separate similar and dissimilar items, treating all non-groundtruth items as equally irrelevant. In this paper we propose to use a variable margin: we argue that varying the margin used during training based on how much relevant an item is to a given query, i.e. a relevance-based margin, easily improves the quality of the ranking lists measured through nDCG and mAP. We demonstrate the advantages of our technique using different models on EPIC-Kitchens-100 and YouCook2. We show that even if we carefully tuned the fixed margin, our technique (which does not have the margin as a hyper-parameter) would still achieve better performance. Finally, extensive ablation studies and qualitative analysis support the robustness of our approach. Code will be released at \urlhttps://github.com/aranciokov/RelevanceMargin-ICMR22.
Address Newwark, NJ, USA, 27 June 2022
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference ICMR
Notes HuPBA; no menciona Approved no
Call Number Admin @ si @ FSS2022 Serial 3808
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Author Ali Furkan Biten; Ruben Tito; Lluis Gomez; Ernest Valveny; Dimosthenis Karatzas
Title OCR-IDL: OCR Annotations for Industry Document Library Dataset Type (up) Conference Article
Year 2022 Publication ECCV Workshop on Text in Everything Abbreviated Journal
Volume Issue Pages
Keywords
Abstract Pretraining has proven successful in Document Intelligence tasks where deluge of documents are used to pretrain the models only later to be finetuned on downstream tasks. One of the problems of the pretraining approaches is the inconsistent usage of pretraining data with different OCR engines leading to incomparable results between models. In other words, it is not obvious whether the performance gain is coming from diverse usage of amount of data and distinct OCR engines or from the proposed models. To remedy the problem, we make public the OCR annotations for IDL documents using commercial OCR engine given their superior performance over open source OCR models. The contributed dataset (OCR-IDL) has an estimated monetary value over 20K US$. It is our hope that OCR-IDL can be a starting point for future works on Document Intelligence. All of our data and its collection process with the annotations can be found in this https URL.
Address
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Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
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Area Expedition Conference ECCV
Notes DAG; no proj Approved no
Call Number Admin @ si @ BTG2022 Serial 3817
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Author Razieh Rastgoo; Kourosh Kiani; Sergio Escalera
Title Real-time Isolated Hand Sign Language RecognitioN Using Deep Networks and SVD Type (up) Journal
Year 2022 Publication Journal of Ambient Intelligence and Humanized Computing Abbreviated Journal
Volume 13 Issue Pages 591–611
Keywords
Abstract One of the challenges in computer vision models, especially sign language, is real-time recognition. In this work, we present a simple yet low-complex and efficient model, comprising single shot detector, 2D convolutional neural network, singular value decomposition (SVD), and long short term memory, to real-time isolated hand sign language recognition (IHSLR) from RGB video. We employ the SVD method as an efficient, compact, and discriminative feature extractor from the estimated 3D hand keypoints coordinators. Despite the previous works that employ the estimated 3D hand keypoints coordinates as raw features, we propose a novel and revolutionary way to apply the SVD to the estimated 3D hand keypoints coordinates to get more discriminative features. SVD method is also applied to the geometric relations between the consecutive segments of each finger in each hand and also the angles between these sections. We perform a detailed analysis of recognition time and accuracy. One of our contributions is that this is the first time that the SVD method is applied to the hand pose parameters. Results on four datasets, RKS-PERSIANSIGN (99.5±0.04), First-Person (91±0.06), ASVID (93±0.05), and isoGD (86.1±0.04), confirm the efficiency of our method in both accuracy (mean+std) and time recognition. Furthermore, our model outperforms or gets competitive results with the state-of-the-art alternatives in IHSLR and hand action recognition.
Address
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Notes HUPBA; no proj Approved no
Call Number Admin @ si @ RKE2022a Serial 3660
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Author Joana Maria Pujadas-Mora; Alicia Fornes; Oriol Ramos Terrades; Josep Llados; Jialuo Chen; Miquel Valls-Figols; Anna Cabre
Title The Barcelona Historical Marriage Database and the Baix Llobregat Demographic Database. From Algorithms for Handwriting Recognition to Individual-Level Demographic and Socioeconomic Data Type (up) Journal
Year 2022 Publication Historical Life Course Studies Abbreviated Journal HLCS
Volume 12 Issue Pages 99-132
Keywords Individual demographic databases; Computer vision, Record linkage; Social mobility; Inequality; Migration; Word spotting; Handwriting recognition; Local censuses; Marriage Licences
Abstract The Barcelona Historical Marriage Database (BHMD) gathers records of the more than 600,000 marriages celebrated in the Diocese of Barcelona and their taxation registered in Barcelona Cathedral's so-called Marriage Licenses Books for the long period 1451–1905 and the BALL Demographic Database brings together the individual information recorded in the population registers, censuses and fiscal censuses of the main municipalities of the county of Baix Llobregat (Barcelona). In this ongoing collection 263,786 individual observations have been assembled, dating from the period between 1828 and 1965 by December 2020. The two databases started as part of different interdisciplinary research projects at the crossroads of Historical Demography and Computer Vision. Their construction uses artificial intelligence and computer vision methods as Handwriting Recognition to reduce the time of execution. However, its current state still requires some human intervention which explains the implemented crowdsourcing and game sourcing experiences. Moreover, knowledge graph techniques have allowed the application of advanced record linkage to link the same individuals and families across time and space. Moreover, we will discuss the main research lines using both databases developed so far in historical demography.
Address June 23, 2022
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Publisher Place of Publication Editor
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Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
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Area Expedition Conference
Notes DAG; 600.121; 600.162; 602.230; 600.140 Approved no
Call Number Admin @ si @ PFR2022 Serial 3737
Permanent link to this record
 
 
Author Parichehr Behjati Ardakani; Pau Rodriguez; Carles Fernandez; Armin Mehri; Xavier Roca; Seiichi Ozawa; Jordi Gonzalez
Title Frequency-based Enhancement Network for Efficient Super-Resolution Type (up) Journal Article
Year 2022 Publication IEEE Access Abbreviated Journal ACCESS
Volume 10 Issue Pages 57383-57397
Keywords Deep learning; Frequency-based methods; Lightweight architectures; Single image super-resolution
Abstract Recently, deep convolutional neural networks (CNNs) have provided outstanding performance in single image super-resolution (SISR). Despite their remarkable performance, the lack of high-frequency information in the recovered images remains a core problem. Moreover, as the networks increase in depth and width, deep CNN-based SR methods are faced with the challenge of computational complexity in practice. A promising and under-explored solution is to adapt the amount of compute based on the different frequency bands of the input. To this end, we present a novel Frequency-based Enhancement Block (FEB) which explicitly enhances the information of high frequencies while forwarding low-frequencies to the output. In particular, this block efficiently decomposes features into low- and high-frequency and assigns more computation to high-frequency ones. Thus, it can help the network generate more discriminative representations by explicitly recovering finer details. Our FEB design is simple and generic and can be used as a direct replacement of commonly used SR blocks with no need to change network architectures. We experimentally show that when replacing SR blocks with FEB we consistently improve the reconstruction error, while reducing the number of parameters in the model. Moreover, we propose a lightweight SR model — Frequency-based Enhancement Network (FENet) — based on FEB that matches the performance of larger models. Extensive experiments demonstrate that our proposal performs favorably against the state-of-the-art SR algorithms in terms of visual quality, memory footprint, and inference time. The code is available at https://github.com/pbehjatii/FENet
Address 18 May 2022
Corporate Author Thesis
Publisher IEEE Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes ISE Approved no
Call Number Admin @ si @ BRF2022a Serial 3747
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Author Pau Riba; Lutz Goldmann; Oriol Ramos Terrades; Diede Rusticus; Alicia Fornes; Josep Llados
Title Table detection in business document images by message passing networks Type (up) Journal Article
Year 2022 Publication Pattern Recognition Abbreviated Journal PR
Volume 127 Issue Pages 108641
Keywords
Abstract Tabular structures in business documents offer a complementary dimension to the raw textual data. For instance, there is information about the relationships among pieces of information. Nowadays, digital mailroom applications have become a key service for workflow automation. Therefore, the detection and interpretation of tables is crucial. With the recent advances in information extraction, table detection and recognition has gained interest in document image analysis, in particular, with the absence of rule lines and unknown information about rows and columns. However, business documents usually contain sensitive contents limiting the amount of public benchmarking datasets. In this paper, we propose a graph-based approach for detecting tables in document images which do not require the raw content of the document. Hence, the sensitive content can be previously removed and, instead of using the raw image or textual content, we propose a purely structural approach to keep sensitive data anonymous. Our framework uses graph neural networks (GNNs) to describe the local repetitive structures that constitute a table. In particular, our main application domain are business documents. We have carefully validated our approach in two invoice datasets and a modern document benchmark. Our experiments demonstrate that tables can be detected by purely structural approaches.
Address July 2022
Corporate Author Thesis
Publisher Elsevier Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes DAG; 600.162; 600.121 Approved no
Call Number Admin @ si @ RGR2022 Serial 3729
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Author Hugo Jair Escalante; Heysem Kaya; Albert Ali Salah; Sergio Escalera; Yagmur Gucluturk; Umut Guçlu; Xavier Baro; Isabelle Guyon; Julio C. S. Jacques Junior; Meysam Madadi; Stephane Ayache; Evelyne Viegas; Furkan Gurpinar; Achmadnoer Sukma Wicaksana; Cynthia Liem; Marcel A. J. Van Gerven; Rob Van Lier
Title Modeling, Recognizing, and Explaining Apparent Personality from Videos Type (up) Journal Article
Year 2022 Publication IEEE Transactions on Affective Computing Abbreviated Journal TAC
Volume 13 Issue 2 Pages 894-911
Keywords
Abstract Explainability and interpretability are two critical aspects of decision support systems. Despite their importance, it is only recently that researchers are starting to explore these aspects. This paper provides an introduction to explainability and interpretability in the context of apparent personality recognition. To the best of our knowledge, this is the first effort in this direction. We describe a challenge we organized on explainability in first impressions analysis from video. We analyze in detail the newly introduced data set, evaluation protocol, proposed solutions and summarize the results of the challenge. We investigate the issue of bias in detail. Finally, derived from our study, we outline research opportunities that we foresee will be relevant in this area in the near future.
Address 1 April-June 2022
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Notes HuPBA; no menciona Approved no
Call Number Admin @ si @ EKS2022 Serial 3406
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Author Mohamed Ali Souibgui; Y.Kessentini
Title DE-GAN: A Conditional Generative Adversarial Network for Document Enhancement Type (up) Journal Article
Year 2022 Publication IEEE Transactions on Pattern Analysis and Machine Intelligence Abbreviated Journal TPAMI
Volume 44 Issue 3 Pages 1180-1191
Keywords
Abstract Documents often exhibit various forms of degradation, which make it hard to be read and substantially deteriorate the performance of an OCR system. In this paper, we propose an effective end-to-end framework named Document Enhancement Generative Adversarial Networks (DE-GAN) that uses the conditional GANs (cGANs) to restore severely degraded document images. To the best of our knowledge, this practice has not been studied within the context of generative adversarial deep networks. We demonstrate that, in different tasks (document clean up, binarization, deblurring and watermark removal), DE-GAN can produce an enhanced version of the degraded document with a high quality. In addition, our approach provides consistent improvements compared to state-of-the-art methods over the widely used DIBCO 2013, DIBCO 2017 and H-DIBCO 2018 datasets, proving its ability to restore a degraded document image to its ideal condition. The obtained results on a wide variety of degradation reveal the flexibility of the proposed model to be exploited in other document enhancement problems.
Address 1 March 2022
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Publisher Place of Publication Editor
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Area Expedition Conference
Notes DAG; 602.230; 600.121; 600.140 Approved no
Call Number Admin @ si @ SoK2022 Serial 3454
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Author Saad Minhas; Aura Hernandez-Sabate; Shoaib Ehsan; Klaus McDonald Maier
Title Effects of Non-Driving Related Tasks during Self-Driving mode Type (up) Journal Article
Year 2022 Publication IEEE Transactions on Intelligent Transportation Systems Abbreviated Journal TITS
Volume 23 Issue 2 Pages 1391-1399
Keywords
Abstract Perception reaction time and mental workload have proven to be crucial in manual driving. Moreover, in highly automated cars, where most of the research is focusing on Level 4 Autonomous driving, take-over performance is also a key factor when taking road safety into account. This study aims to investigate how the immersion in non-driving related tasks affects the take-over performance of drivers in given scenarios. The paper also highlights the use of virtual simulators to gather efficient data that can be crucial in easing the transition between manual and autonomous driving scenarios. The use of Computer Aided Simulations is of absolute importance in this day and age since the automotive industry is rapidly moving towards Autonomous technology. An experiment comprising of 40 subjects was performed to examine the reaction times of driver and the influence of other variables in the success of take-over performance in highly automated driving under different circumstances within a highway virtual environment. The results reflect the relationship between reaction times under different scenarios that the drivers might face under the circumstances stated above as well as the importance of variables such as velocity in the success on regaining car control after automated driving. The implications of the results acquired are important for understanding the criteria needed for designing Human Machine Interfaces specifically aimed towards automated driving conditions. Understanding the need to keep drivers in the loop during automation, whilst allowing drivers to safely engage in other non-driving related tasks is an important research area which can be aided by the proposed study.
Address Feb. 2022
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Notes IAM; 600.139; 600.145 Approved no
Call Number Admin @ si @ MHE2022 Serial 3468
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Author Jun Wan; Chi Lin; Longyin Wen; Yunan Li; Qiguang Miao; Sergio Escalera; Gholamreza Anbarjafari; Isabelle Guyon; Guodong Guo; Stan Z. Li
Title ChaLearn Looking at People: IsoGD and ConGD Large-scale RGB-D Gesture Recognition Type (up) Journal Article
Year 2022 Publication IEEE Transactions on Cybernetics Abbreviated Journal TCIBERN
Volume 52 Issue 5 Pages 3422-3433
Keywords
Abstract The ChaLearn large-scale gesture recognition challenge has been run twice in two workshops in conjunction with the International Conference on Pattern Recognition (ICPR) 2016 and International Conference on Computer Vision (ICCV) 2017, attracting more than 200 teams round the world. This challenge has two tracks, focusing on isolated and continuous gesture recognition, respectively. This paper describes the creation of both benchmark datasets and analyzes the advances in large-scale gesture recognition based on these two datasets. We discuss the challenges of collecting large-scale ground-truth annotations of gesture recognition, and provide a detailed analysis of the current state-of-the-art methods for large-scale isolated and continuous gesture recognition based on RGB-D video sequences. In addition to recognition rate and mean jaccard index (MJI) as evaluation metrics used in our previous challenges, we also introduce the corrected segmentation rate (CSR) metric to evaluate the performance of temporal segmentation for continuous gesture recognition. Furthermore, we propose a bidirectional long short-term memory (Bi-LSTM) baseline method, determining the video division points based on the skeleton points extracted by convolutional pose machine (CPM). Experiments demonstrate that the proposed Bi-LSTM outperforms the state-of-the-art methods with an absolute improvement of 8.1% (from 0.8917 to 0.9639) of CSR.
Address May 2022
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Notes HUPBA; no menciona Approved no
Call Number Admin @ si @ WLW2022 Serial 3522
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Author Daniela Rato; Miguel Oliveira; Vitor Santos; Manuel Gomes; Angel Sappa
Title A sensor-to-pattern calibration framework for multi-modal industrial collaborative cells Type (up) Journal Article
Year 2022 Publication Journal of Manufacturing Systems Abbreviated Journal JMANUFSYST
Volume 64 Issue Pages 497-507
Keywords Calibration; Collaborative cell; Multi-modal; Multi-sensor
Abstract Collaborative robotic industrial cells are workspaces where robots collaborate with human operators. In this context, safety is paramount, and for that a complete perception of the space where the collaborative robot is inserted is necessary. To ensure this, collaborative cells are equipped with a large set of sensors of multiple modalities, covering the entire work volume. However, the fusion of information from all these sensors requires an accurate extrinsic calibration. The calibration of such complex systems is challenging, due to the number of sensors and modalities, and also due to the small overlapping fields of view between the sensors, which are positioned to capture different viewpoints of the cell. This paper proposes a sensor to pattern methodology that can calibrate a complex system such as a collaborative cell in a single optimization procedure. Our methodology can tackle RGB and Depth cameras, as well as LiDARs. Results show that our methodology is able to accurately calibrate a collaborative cell containing three RGB cameras, a depth camera and three 3D LiDARs.
Address
Corporate Author Thesis
Publisher Science Direct Place of Publication Editor
Language Summary Language Original Title
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Area Expedition Conference
Notes MSIAU; MACO Approved no
Call Number Admin @ si @ ROS2022 Serial 3750
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