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Optimisation of a Siamese Neural Network for Real-Time Energy Efficient Object Tracking 1

International Conference on computer vision and Graphics, ICCVG 2020

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

ISBN: (数字)9783030590062

ISBN: (纸本)9783030590055

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, Springer Nature Switzerland AG.

关键词： Field programmable gate arrays (FPGA)

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Traffic Sign Classification Using Deep and Quantum Neural Networks

Traffic Sign Classification Using Deep and Quantum Neural Ne...

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International Conference on computer vision and Graphics, ICCVG 2022

作者： Kuros, Sylwia Kryjak, Tomasz Embedded Vision Systems Group Computer Vision Laboratory Department of Automatic Control and Robotics AGH University of Science and Technology Krakow Poland

ISBN: (纸本)9783031220241

Quantum Neural Networks (QNNs) are an emerging technology that can be used in many applications including computer vision. In this paper, we presented a traffic sign classification system implemented using a hybrid quantum-classical convolutional neural network. Experiments on the German Traffic Sign Recognition Benchmark dataset indicate that currently QNN do not outperform classical DCNN (Deep Convolutuional Neural Networks), yet still provide an accuracy of over 90% and are a definitely promising solution for advanced computer vision. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Traffic signs

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A vision Based Hardware-Software Real-Time Control System for the Autonomous Landing of an UAV 1

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International Conference on computer vision and Graphics, ICCVG 2020

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

ISBN: (数字)9783030590062

ISBN: (纸本)9783030590055

In this paper we present a vision based hardware-software control system enabling the autonomous landing of a multirotor unmanned aerial vehicle (UAV). It allows for the detection of a marked landing pad in real-time for a 1280 720 @ 60 fps video stream. In addition, a LiDAR sensor is used to measure the altitude above ground. A heterogeneous Zynq SoC device is used as the computing platform. The solution was tested on a number of sequences and the landing pad was detected with 96% accuracy. This research shows that a reprogrammable heterogeneous computing system is a good solution for UAVs because it enables real-time data stream processing with relatively low energy consumption. © 2020, Springer Nature Switzerland AG.

关键词： System-on-chip

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A Review of Deep Learning Techniques for Glaucoma Detection

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SN computer science 2023年第3期4卷 274页

作者： Guergueb, Takfarines Akhloufi, Moulay A. Perception Robotics and Intelligent Machines Research Group (PRIME) Computer Science Department Université de Moncton Moncton NB Canada

Glaucoma is one of the major reasons for visual impairment all across the globe. The recent advancements in machine learning techniques have greatly facilitated ophthalmologists in the early diagnosis of ocular diseases through the employment of automated systems. Several studies have been published lately to address the timely detection of glaucoma using deep learning approaches. A comprehensive review of the deep learning approaches employed for glaucoma detection using retinal fundus images is presented in this paper. The available retinal image datasets, image pre-processing techniques, state-of-the-art models, and performance evaluation metrics used in the recent studies are reviewed. This systematic review aims to provide critical insights and potential research directions to the ophthalmologists and researchers in this domain. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Deep learning Eye diseases Glaucoma Image processing Machine learning Ophthalmology Transfer learning

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Supervised texture segmentation through a multi-level pixel-based classifier based on specifically designed filters

Supervised texture segmentation through a multi-level pixel-...

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IEEE International Conference on Image Processing

作者： Jaime Melendez Xavier Girones Domenec Puig Intelligent Robotics and Computer Vision Group Department of Computer Science and Mathematics Rovira i Virgili University Spain

This paper presents a new, efficient technique for supervised texture segmentation based on a set of specifically designed filters and a multi-level pixel-based classifier. Filter design is carried out by means of a neural network, which is trained to maximize the filters' discrimination power among the texture classes under consideration. Texture features obtained with these filters are then processed by a classification scheme that utilizes multiple evaluation window sizes following a top-down approach, which iteratively refines the resulting segmentation. The proposed technique is compared to previous supervised texture segmenters by using both synthetic compositions and real outdoor textured images.

关键词： Filter banks Image segmentation Feature extraction Gabor filters Adaptive filters Conferences Support vector machines

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Conditional Affordance Learning for Driving in Urban Environments 2

Conditional Affordance Learning for Driving in Urban Environ...

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2nd Conference on Robot Learning, CoRL 2018

作者： Sauer, Axel Savinov, Nikolay Geiger, Andreas Computer Vision and Geometry Group ETH Zürich Switzerland Robotics Science and System Intelligence Technical University of Munich Germany Autonomous Vision Group MPI for Intelligent Systems University of Tübingen Germany

Most existing approaches to autonomous driving fall into one of two categories: modular pipelines, that build an extensive model of the environment, and imitation learning approaches, that map images directly to control outputs. A recently proposed third paradigm, direct perception, aims to combine the advantages of both by using a neural network to learn appropriate low-dimensional intermediate representations. However, existing direct perception approaches are restricted to simple highway situations, lacking the ability to navigate intersections, stop at traffic lights or respect speed limits. In this work, we propose a direct perception approach which maps video input to intermediate representations suitable for autonomous navigation in complex urban environments given high-level directional inputs. Compared to state-of-the-art reinforcement and conditional imitation learning approaches, we achieve an improvement of up to 68 % in goal-directed navigation on the challenging CARLA simulation benchmark. In addition, our approach is the first to handle traffic lights and speed signs by using image-level labels only, as well as smooth car-following, resulting in a significant reduction of traffic accidents in simulation. © CoRL 2018. All rights reserved.

关键词： Autonomous vehicles

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Recognizing specific texture patterns by integration of multiple texture methods

Recognizing specific texture patterns by integration of mult...

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IEEE International Conference on Image Processing

作者： D. Puig M.A. Garcia Intelligent Robotics and Computer Vision Group Department of Computer Science and Mathematics Rovira i Virgili University Tarragona Spain

A supervised pixel-based classifier for identifying the presence of a given set of texture patterns of interest in a complex textured image is described. The proposed technique integrates the outcome of multiple texture feature extraction methods belonging to different families. In this way, it yields lower classification rates than previous texture classifiers based on specific families of texture methods. Experimental results with real outdoor images are presented.

关键词： Pattern recognition Pixel Image recognition Feature extraction intelligent robots Layout Image segmentation Robot vision systems computer vision computer science

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Pixel classification through divergence-based integration of texture methods with conflict resolution

Pixel classification through divergence-based integration of...

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IEEE International Conference on Image Processing

作者： D. Puig M.A. Garcia Intelligent Robotics and Computer Vision Group Department of Computer Science and Mathematics Rovira i Virgili University Tarragona Spain

This paper presents a new technique for combining multiple texture feature extraction methods in order to classify the pixels of an input image into a set of texture models of interest. The problem of integrating multiple texture methods for classification purposes is cast as a collaborative decision making problem. Each texture method is considered to be an expert that gives an opinion about the membership of every input image pixel to each texture model, along with a conviction about that judgement. A conviction measure based on the Kullback J-divergence between texture models is proposed, along with an arbitration mechanism that combines those convictions by taking into account conflicts that may occur when different experts disagree with a similar strength. The proposed technique is compared to previous pixel-based texture classifiers by using real textured images.

关键词： Pixel intelligent robots Feature extraction Collaboration Decision making Robot vision systems computer vision computer science mathematics Soil

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3D simultaneous localization and modeling from stereo vision

3D simultaneous localization and modeling from stereo vision

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IEEE International Conference on robotics and Automation (ICRA)

作者： M.A. Garcia A. Solanas Intelligent Robotics and Computer Vision Group /Department of Computer Science and Mathematics Rovira i Virgili University Tarragona Spain

This work presents a new algorithm for determining the trajectory of a mobile robot and, simultaneously, creating a detailed volumetric 3D model of its workspace. The algorithm exclusively utilizes information provided by a single stereo vision system, avoiding thus the use both of more costly laser systems and error-prone odometry. Six-degrees-of-freedom egomotion is directly estimated from images acquired at relatively close positions along the robot's path. Thus, the algorithm can deal with both planar and uneven terrain in a natural way, without requiring extra processing stages or additional orientation sensors. The 3D model is based on an octree that encapsulates clouds of 3D points obtained through stereo vision, which are integrated after each egomotion stage. Every point has three spatial coordinates referred to a single frame, as well as true-color components. The spatial location of those points is continuously improved as new images are acquired and integrated into the model.

关键词： Stereo vision Simultaneous localization and mapping Robot kinematics Robot sensing systems Mobile robots intelligent robots Robot vision systems Laser modes Mathematical model computer vision

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Coordinated multi-robot exploration through unsupervised clustering of unknown space

Coordinated multi-robot exploration through unsupervised clu...

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IEEE International Workshop on intelligent Robots and Systems (IROS)

作者： A. Solanas M.A. Garcia Intelligent Robotics and Computer Vision Group/Department of Computer Science and Mathematics Rovira i Virgili University Tarragona Spain

This paper proposes a new coordination algorithm for efficiently exploring an unknown environment with a team of mobile robots. The proposed technique subsequently applies a well-known unsupervised clustering algorithm (k-means) in order to fairly divide the remaining unknown space into as many disjoint regions as available robots. Each robot is primarily responsible for exploring its assigned region and can help other robots on its way through. Unknown space is dynamically repartitioned as new areas are discovered by the team, balancing thus the overall workload among team members and naturally leading to greater dispersion over the environment and thus faster broad coverage than with previous greedy-like approaches, which guide robots based on maximum profit strategies that simply trade off between distance to the closest frontiers and amount of unknown cells likely to be discovered from them.

关键词： Robot kinematics Orbital robotics Robot sensing systems Mobile robots Space technology intelligent robots Layout computer science mathematics Clustering algorithms

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