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arXiv 2021年

作者： Bhayani, Snehal Sattler, Torsten Barath, Daniel Beliansky, Patrik Heikkilä, Janne Kukelova, Zuzana Center for Machine Vision and Signal Analysis University of Oulu Finland Czech Institute of Informatics Robotics and Cybernetics Czech Technical University in Prague Computer Vision and Geometry Group Department of Computer Science ETH Zürich Faculty of Mathematics and Physics Charles University Prague Czech Republic Visual Recognition Group Faculty of Electrical Engineering Czech Technical University in Prague

In this paper, we propose the first minimal solutions for estimating the semi-generalized homography given a perspective and a generalized camera. The proposed solvers use five 2D-2D image point correspondences induced by a scene plane. One group of solvers assumes the perspective camera to be fully calibrated, while the other estimates the unknown focal length together with the absolute pose parameters. This setup is particularly important in structurefrom-motion and visual localization pipelines, where a new camera is localized in each step with respect to a set of known cameras and 2D-3D correspondences might not be available. Thanks to a clever parametrization and the elimination ideal method, our solvers only need to solve a univariate polynomial of degree five or three, respectively a system of polynomial equations in two variables. All proposed solvers are stable and efficient as demonstrated by a number of synthetic and real-world experiments. Copyright © 2021, The Authors. All rights reserved.

关键词： Cameras

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Platform-portable reinforcement learning methods to localize underwater targets

Platform-portable reinforcement learning methods to localize...

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OCEANS

作者： Ivan Masmitja Mario Martin Tom O’Reilly Narcis Palomeras Kakani Katija Joan Navarro Institut de Ciències del Mar (ICM) CSIC Barcelona Spain KEMLG Research Group Universitat Politècnica de Catalunya Barcelona Tech. Barcelona Spain Monterey Bay Aquarium Research Institute Moss Landing USA Computer Vision and Robotics Institute Universitat de Girona Girona Spain Bioinspiration Lab Monterey Bay Aquarium Research Institute Moss Landing USA

Subsea residential autonomous systems will reduce the cost and CO 2 footprint of future subsea operations. This concept includes subsea docking stations (SDS) and Autonomous Underwater Vehicles (AUVs). To ensure the AUVs are capable of interfacing with the SDS, a driftless and accurate close-range navigation schema is required. Thus, this paper proposes a visual method for navigation utilizing a defined set of ArUco markers printed on the SDS. To increase the accuracy of the system, the set of ArUco markers is defined to a common frame of reference, introducing more points in the camera frame for a more accurate solution to the Perspective-n-Point problem. This makes the system robust against partial occlusion. The paper has also implemented a modified Line of Sight (LOS) schema for the guidance of the vehicle. The results show stable pose estimates at up to 6 meters above the SDS, and successful autonomous docking of an underwater vehicle. The navigational estimates provided by the system get more stable when the vehicle is closer to the SDS.

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Towards efficient human-robot cooperation for socially-aware robot navigation in human-populated environments: the SNAPE framework

Towards efficient human-robot cooperation for socially-aware...

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IEEE International Conference on robotics and Automation (ICRA)

作者： A. Vega-Magro R. Gondkar L.J. Manso P. Núñez Robotics and Artificial Vision Lab. RoboLab Group University of Extremadura Spain Pune Institute of Computer Technology Pune University India College of Engineering and Physical Sciences Aston University Birmingham UK

It is widely accepted that in the future, robots will cooperate with humans in everyday tasks. Robots interacting with humans will require social awareness when performing their tasks which will require navigation. While navigating, robots should aim to avoid distressing people in order to maximize their chance of social acceptance. For instance, avoiding getting too close to people or disrupting interactions. Most research approaches these problems by planning socially accepted paths, however, in everyday situations, there are many examples where a simple path planner cannot solve all of the predicted robots’ navigation problems. For instance, requesting permission to interrupt a conversation if an alternative path cannot be determined requires deliberative skills. This article presents the Social Navigation framework for Autonomous robots in Populated Environments (SNAPE), where different software agents are integrated within a robotics cognitive architecture. SNAPE addresses action planning aimed at social-awareness navigation in realistic situations: it plans socially accepted paths and conversations to negotiate its trajectory to reach targets. In this article, the framework is evaluated in different use-cases where the robot, during its navigation, has to interact with different people in order to reach its goal. The results show that participants report that the robot’s behavior was realistic and human-like.

关键词： Navigation Conferences computer architecture Software agents Planning Trajectory Task analysis

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Optimisation of a siamese neural network for real-time energy efficient object tracking

TechRxiv

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TechRxiv 2020年

作者： Przewlocka, Dominika Wasala, Mateusz Szolc, Hubert Blachut, Krzysztof Kryjak, Tomasz Embedded Vision Systems Group Computer Vision Laboratory Department of Automatic Control and Robotics AGH University of Science and Technology Krakow Poland

In this paper the research on optimisation of visual object tracking using a Siamese neural network for embedded vision systems is presented. It was assumed that the solution shall operate in real-time, preferably for a high resolution video stream, with the lowest possible energy consumption. To meet these requirements, techniques such as the reduction of computational precision and pruning were considered. Brevitas, a tool dedicated for optimisation and quantisation of neural networks for FPGA implementation, was used. A number of training scenarios were tested with varying levels of optimisations – from integer uniform quantisation with 16 bits to ternary and binary networks. Next, the influence of these optimisations on the tracking performance was evaluated. It was possible to reduce the size of the convolutional filters up to 10 times in relation to the original network. The obtained results indicate that using quantisation can significantly reduce the memory and computational complexity of the proposed network while still enabling precise tracking, thus allow to use it in embedded vision systems. Moreover, quantisation of weights positively affects the network training by decreasing overfitting. © 2020, CC BY-NC-SA.

关键词： Field programmable gate arrays (FPGA)

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Optimisation of a siamese neural network for real-time energy efficient object tracking

arXiv

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arXiv 2020年

In this paper the research on optimisation of visual object tracking using a Siamese neural network for embedded vision systems is presented. It was assumed that the solution shall operate in real-time, preferably for a high resolution video stream, with the lowest possible energy consumption. To meet these requirements, techniques such as the reduction of computational precision and pruning were considered. Brevitas, a tool dedicated for optimisation and quantisation of neural networks for FPGA implementation, was used. A number of training scenarios were tested with varying levels of optimisations - from integer uniform quantisation with 16 bits to ternary and binary networks. Next, the influence of these optimisations on the tracking performance was evaluated. It was possible to reduce the size of the convolutional filters up to 10 times in relation to the original network. The obtained results indicate that using quantisation can significantly reduce the memory and computational complexity of the proposed network while still enabling precise tracking, thus allow to use it in embedded vision systems. Moreover, quantisation of weights positively affects the network training by decreasing overfitting. Copyright © 2020, The Authors. All rights reserved.

关键词： Field programmable gate arrays (FPGA)

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Integration of online Laboratories in Learning Platforms

Integration of online Laboratories in Learning Platforms

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Technologies Applied to Electronics Teaching (TAEE)

作者： Ildefonso Ruano Ruano Elísabet Estévez Estévez Alejandro Sánchez García Javier Gámez García Juan Gómez Ortega Group of Robotics Automation and Computer Vision (GRAV) Universidad de Jaén Jaén Spain

ISBN: (数字)9781728167329

ISBN: (纸本)9781728167336

This work dials with the integration of online laboratories into Learning Management Systems. In fact, this work emphasizes the pedagogical advantages that could be achieved with this integration as well as the involved actors, being mainly: students, lecturers and laboratory owners. It also makes an analysis of each actor's requirements and how they can be satisfied by the integration. To achieve this, the use of standards is essential. Authors use an example of online laboratory and integrate it into LMSs by different paths but using the more widespread standards. Finally, the achieved advantages are discussed.

关键词： Laboratories Standards Electronic learning Software Robots Automation

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Calibrated and Partially Calibrated Semi-Generalized Homographies

Calibrated and Partially Calibrated Semi-Generalized Homogra...

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International Conference on computer vision (ICCV)

作者： Snehal Bhayani Torsten Sattler Daniel Barath Patrik Beliansky Janne Heikkilä Zuzana Kukelova Center for Machine Vision and Signal Analysis University of Oulu Finland Czech Institute of Informatics Robotics and Cybernetics Czech Technical University in Prague Computer Vision and Geometry Group ETH Zürich Faculty of Mathematics and Physics Charles University Prague Visual Recognition Group Faculty of Electrical Engineering Czech Technical University in Prague

ISBN: (纸本)9781665428132

In this paper, we propose the first minimal solutions for estimating the semi-generalized homography given a perspective and a generalized camera. The proposed solvers use five 2D-2D image point correspondences induced by a scene plane. One group of solvers assumes the perspective camera to be fully calibrated, while the other estimates the unknown focal length together with the absolute pose parameters. This setup is particularly important in structure-from-motion and visual localization pipelines, where a new camera is localized in each step with respect to a set of known cameras and 2D-3D correspondences might not be available. Thanks to a clever parametrization and the elimination ideal method, our solvers only need to solve a univariate polynomial of degree five or three, respectively a system of polynomial equations in two variables. All proposed solvers are stable and efficient as demonstrated by a number of synthetic and real-world experiments.

关键词： Location awareness Visualization computer vision Pipelines Focusing Cameras

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Ridge polynomial neural network with error feedback for recursive multi-step forecast strategy: A case study of carbon dioxide emissions forecasting 2

Ridge polynomial neural network with error feedback for recu...

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2nd International Conference on computer and Information Sciences, ICCIS 2020

作者： Waheeb, Waddah Shah, Habib Jabreel, Mohammed Puig, Domenec School of Computing Asia Pacific University of Technology Innovation Kuala Lumpur Bukit Jalil57000 Malaysia College of Computer Science King Khalid University Department of Computer Science Abha62529 Saudi Arabia IRCV - Intelligent Robotics and Computer Vision Group Universitat Rovira i Virgili Tarragona43007 Spain

ISBN: (数字)9781728154671

ISBN: (纸本)9781728154671

Neural networks (NNs) have been used extensively for forecasting problems. NN with error feedbacks is a type of NNs that showed more accurate forecasts compared to feedforward NNs and NNs with output feedbacks with some forecasting problems. The main issue with NN s with error feedbacks appears when there is a need for recursive multi-step forecast strategy because the observed values must be known in order to calculate network errors. This paper proposes to use the last calculated error after finishing training NNs with error feedbacks because the observed values are unknown. This last calculated error is used as a fixed value when producing forecasts using recursive multi-step forecast strategy. For that, this paper investigated this simple solution with a NN with error feedback called the ridge polynomial neural network with error feedback (RPNN-EF). Carbon dioxide emissions for three countries in the organization of the petroleum exporting countries (OPEC) were used in this investigation. The forecasting accuracy of RPNN-EF was compared with seven forecasting methods. According to the obtained results, on average, the proposed solution produces reasonable forecasts compared to the seven forecasting methods. Therefore, this solution can be suggested for NNs with error feedbacks for recursive multi-step forecast strategy. © 2020 IEEE.

关键词： Polynomials

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Bitcoin price forecasting: A comparative study between statistical and machine learning methods 2

Bitcoin price forecasting: A comparative study between stati...

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2nd International Conference on computer and Information Sciences, ICCIS 2020

ISBN: (纸本)9781728154671

This paper presents a comparative study between statistical and machine learning methods in forecasting Bitcoin's closing prices. Thirteen forecasting methods namely average, naive, drift, auto-regressive integrated moving-average, simple exponential smoothing (SES), Holt, and damped exponential smoothing, the average of SES, Holt and damped methods, exponential smoothing (ETS), bagged ETS, Theta, multilayer perceptron, and extreme learning machines (ELM) were used to forecast the closing prices for the next 14 days. The findings of this study are three folds. First, there are seven forecasting methods outperformed the naive method namely MLP, ELM, damped exponential smoothing, simple exponential smoothing, Theta, ETS, and ARIMA. Second, MLP and ELM showed better forecasting accuracy on both validation and out-of-sample data among the forecasting methods used in this study. Third, the size of the training data is essential factor that should be considered when training forecasting methods. © 2020 IEEE.

关键词： Bitcoin

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Multiparametric benthic landers for monitoring fishing-impacted deep-sea ecosystems

Multiparametric benthic landers for monitoring fishing-impac...

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OCEANS

作者： Daniel Mihai Toma Jacopo Aguzzi Matias Carandell Marc Nogueras Enoc Martínez Marco Francescangeli Damianos Chatzievangelou Nixon Bahamon Joan Baptista Company Jordi Grinyo Marc Carreras Sascha Flögel Joaquín del Río SARTI Research Group Universitat Politècnica de Catalunya Vilanova i la Geltrú Spain Instituto de Ciencias del Mar Consejo Superior de Investigaciones Científicas Barcelona Spain Department of Ocean Systems NIOZ Royal Netherlands Institute for Sea Research Den Burg The Netherlands Computer Vision and Robotics Institute University of Girona Girona Spain GEOMAR Helmholtz Centre for Ocean Research Kiel Kiel Germany

To assess conservation efforts and follow the effects of anthropogenic pressures, it is crucial to monitor the ecological status of benthic habitats. Monitoring requires measurements that are made on-site, reproduced over a vast region, and capable of capturing their spatial evolution over time. This calls for the expensively efficient development of tools and protocols that deliver reliable, high-quality data. This article introduces the PLOME multiparametric benthic landers, a new tool and protocol for non-extractively assessing the status of fishes and benthic habitats. By combining independent and wirelessly connected benthic stations, AUVs, and USVs, PLOME creates a spatially adjustable, non-invasive, modular platform. Lander stations will be able to continuously deliver high-rate data while AUVs can provide comprehensive spatial measurements.

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