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Spotlight Control for Real-Time Targeting 32

Spotlight Control for Real-Time Targeting

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32nd International Conference in Central Europe on computer graphics, Visualization and computer vision, WSCG 2024

作者： Heinemann, Mika Benjamin Kernbauer, Thomas Fleck, Philipp Arth, Clemens Institute of Computer Graphics and Vision University of Technology Graz Austria

Off-road heavy machinery such as snow groomers or excavators, often operate in low-light and hazardous environments. In this work, we explore the development of an intelligent camera-spotlight system with automatic and manual control to illuminate points of interest, such as obstacles or individuals at risk. We implement a prototype as proof of concept and integrate our workflow using a standard lighting protocol and a single-board computer. The presented calibration of the camera and spotlight ensures high precision in the desired use cases. In addition to testing the prototype on a real snow groomer, we evaluated its performance in terms of accuracy and repeatability. Overall, we showcase the usability of a commercially available spotlight in the context of spatial augmented reality in heavy machinery applications. © 2024 University of West Bohemia. All rights reserved.

关键词： Augmented reality

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SAda-Net: A Self-supervised Adaptive Stereo Estimation CNN For Remote Sensing Image Data 27th

SAda-Net: A Self-supervised Adaptive Stereo Estimation CNN F...

引用

27th International Conference on Pattern Recognition, ICPR 2024

作者： Hirner, Dominik Fraundorfer, Friedrich Institute of Computer Graphics and Vision Graz University of Technology Graz Austria Cologne Germany

ISBN: (纸本)9783031781919

Stereo estimation has made many advancements in recent years with the introduction of deep-learning. However the traditional supervised approach to deep-learning requires the creation of accurate and plentiful ground-truth data, which is expensive to create and not available in many situations. This is especially true for remote sensing applications, where there is an excess of available data without proper ground truth. To tackle this problem, we propose a self-supervised CNN with self-improving adaptive abilities. In the first iteration, the created disparity map is inaccurate and noisy. Leveraging the left-right consistency check, we get a sparse but more accurate disparity map which is used as an initial pseudo ground-truth. This pseudo ground-truth is then adapted and updated after every epoch in the training step of the network. We use the sum of inconsistent points in order to track the network convergence. The code for our method will be made available after acceptance at https://***/thedodo/SAda-Net. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Self-supervised learning

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Test-time Adversarial Detection and Robustness for Localizing Humans Using Ultra Wide Band Channel Impulse Responses 31

Test-time Adversarial Detection and Robustness for Localizin...

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31st European Signal Processing Conference, EUSIPCO 2023

作者： Kolli, Abhiram Mirza, Muhammad Jehanzeb Possegger, Horst Bischof, Horst Institute of Computer Graphics and Vision Graz University of Technology Austria

ISBN: (纸本)9789464593600

Keyless entry systems in cars are adopting neural networks for localizing its operators. Using test-time adversarial defences equip such systems with the ability to defend against adversarial attacks without prior training on adversarial samples. We propose a test-time adversarial example detector which detects the input adversarial example through quantifying the localized intermediate responses of a pre-trained neural network and confidence scores of an auxiliary softmax layer. Furthermore, in order to make the network robust, we extenuate the non-relevant features by non-iterative input sample clipping. Using our approach, mean performance over 15 levels of adversarial perturbations is increased by 53.3% for the fast gradient sign method and 60.9% for both the basic iterative method and the projected gradient method when compared to adversarial training. © 2023 European Signal Processing Conference, EUSIPCO. All rights reserved.

关键词： Impulse response

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Free-Viewpoint Visual Inspection via 3D Gaussian Splatting for Direct Template Matching 50

Free-Viewpoint Visual Inspection via 3D Gaussian Splatting f...

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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Ito, Kenta Ueda, Shiori Mori, Shohei Sugano, Junichi Adachi, Hideyuki Saito, Hideo Information and Computer Science Keio University Yokohama Japan Institute of Computer Graphics and Vision Graz University of Technology Graz Austria ViSCO Technologies Corporation Tokyo Japan

ISBN: (纸本)9781665464543

Machine vision systems play a pivotal role in streamlining manufacturing processes, notably in quality control through automatic in-line visual inspections. A common practice for inspecting parts, components, and final products is to use a master part benchmark for quality comparison. However, challenges arise when objects enter inspection points in unintended orientations. This misalignment potentially leads to erroneous decisions by automated systems, resulting in additional checkpoints or wastage affecting the production rate. To tackle this issue, we propose a visual inspection pipeline that leverages recent machine learning-based approaches to compare the inspection target and a master part virtually oriented to the same perspective. Specifically, we suggest combining 3D Gaussian Splatting and DUSt3R as a practical solution. Our approach demonstrates its efficacy in real-world scenarios through testing on three mock parts and a real industrial component. © 2024 IEEE.

关键词： Template matching

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Towards Neuroadaptive Augmented Reality Piano Tutorials 2

Towards Neuroadaptive Augmented Reality Piano Tutorials

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2nd Edition IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering, MetroXRAINE 2023

作者： Maitz, Florian Ribeiro Skreinig, Lucchas Kalkofen, Denis Wriessnegger, Selina C. Institute of Neural Engineering Graz University of Technology Graz Austria Institute of Computer Graphics and Vision Graz University of Technology Graz Austria

ISBN: (纸本)9798350300802

EEG-based neuro-adaptive systems are becoming increasingly popular in the field of learning and education. Their ability to detect changes in brain activity in real-time makes them unique and enables monitoring mental workload (MWL) and fatigue. We present a neuro-adaptive system that combines an Augmented Reality (AR) piano tutorial with online EEG measurements of its user, delivered by a passive Brain computer Interface (BCI) system. The MWL was measured by means of EEG and a Filter Bank Common Spatial Patter algorithm (FBCSP) was trained to differentiate between low and high levels of MWL. Low levels were connected to the 0-back task and high levels to the 2-back task. The n-back task was a calibration task to train a binary Machine Learning (ML) classifier to differentiate between low and high MWL. This trained ML algorithm was then used as the central element of the passive BCI, which constantly classified small windows of the EEG during the piano tutorial and adapted the difficulty of the tutorial. 22 Participants were randomly separated into two groups: adaptive and non-adaptive piano tutorial. The results of the non-adaptive group showed significantly higher levels of classified MWL throughout the piano tutorial. © 2023 IEEE.

关键词： EEG mental workload Mixed Reality passive BCI

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TAEC: Unsupervised action segmentation with temporal-Aware embedding and clustering 26

TAEC: Unsupervised action segmentation with temporal-Aware e...

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26th computer vision Winter Workshop, CVWW 2023

作者： Lin, Wei Kukleva, Anna Possegger, Horst Kuehne, Hilde Bischof, Horst Institute of Computer Graphics and Vision Graz University of Technology Austria Christian Doppler Laboratory for Semantic 3D Computer Vision Austria Max-Planck-Institute for Informatics Germany Goethe University Frankfurt Germany

Temporal action segmentation in untrimmed videos has gained increased attention recently. However, annotating action classes and frame-wise boundaries is extremely time consuming and cost intensive, especially on large-scale datasets. To address this issue, we propose an unsupervised approach for learning action classes from untrimmed video sequences. In particular, we propose a temporal embedding network that combines relative time prediction, feature reconstruction, and sequence-To-sequence learning, to preserve the spatial layout and sequential nature of the video features. A two-step clustering pipeline on these embedded feature representations then allows us to enforce temporal consistency within, as well as across videos. Based on the identified clusters, we decode the video into coherent temporal segments that correspond to semantically meaningful action classes. Our evaluation on three challenging datasets shows the impact of each component and, furthermore, demonstrates our state-of-The-Art unsupervised action segmentation results. © 2023 Copyright for this paper by its authors.

关键词： Large dataset

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Spatial Augmented Reality for Heavy Machinery Using Laser Projections

Spatial Augmented Reality for Heavy Machinery Using Laser Pr...

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Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), IEEE Conference on

作者： Thomas Kernbauer Maximilian Tschulik Philipp Fleck Clemens Arth Institute of Computer Graphics and Vision Graz University of Technology

ISBN: (数字)9798350374490

ISBN: (纸本)9798350374506

Operating heavy machinery is challenging and can pose safety hazards for the operator and bystanders. Although commonly used augmented reality (AR) devices, such as head-mounted or head-up displays, can provide occupational support to operators, they can also cause problems. Particularly in off-highway scenarios, i.e., when driving machines in bumpy environments, the usefulness of current AR devices and the willingness of operators to wear them are limited. Therefore, we explore how laser-projection-based AR can help the operator facilitate their tasks and enhance safety. For this, we present a compact hardware unit and introduce a flexible and declarative software system. Furthermore, we examine the calibration process to leverage a camera projector setup and outline a process for creating images suitable for display by a laser projector from a set of line segments. Finally, we showcase its ability to provide efficient instructions to operators and bystanders and propose concrete applications for our setup.

关键词： Solid modeling Three-dimensional displays User interfaces Distortion Cameras Software systems Hardware

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Efficient Motion Prediction: A Lightweight & Accurate Trajectory Prediction Model With Fast Training and Inference Speed

Efficient Motion Prediction: A Lightweight & Accurate Trajec...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Alexander Prutsch Horst Bischof Horst Possegger Institute of Computer Graphics and Vision Graz University of Technology

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

For efficient and safe autonomous driving, it is essential that autonomous vehicles can predict the motion of other traffic agents. While highly accurate, current motion prediction models often impose significant challenges in terms of training resource requirements and deployment on embedded hardware. We propose a new efficient motion prediction model, which achieves highly competitive benchmark results while training only a few hours on a single GPU. Due to our lightweight architectural choices and the focus on reducing the required training resources, our model can easily be applied to custom datasets. Furthermore, its low inference latency makes it particularly suitable for deployment in autonomous applications with limited computing resources.

关键词： Training Accuracy Computational modeling graphics processing units computer architecture Predictive models Transformers Trajectory Autonomous vehicles Standards

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HAMMER: Learning Entropy Maps to Create Accurate 3D Models in Multi-View Stereo

HAMMER: Learning Entropy Maps to Create Accurate 3D Models i...

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IEEE Workshop on Applications of computer vision (WACV)

作者： Rafael Weilharter Friedrich Fraundorfer Institute of Computer Graphics and Vision Graz University of Technology

While the majority of recent Multi-View Stereo Networks estimates a depth map per reference image, their performance is then only evaluated on the fused 3D model obtained from all images. This approach makes a lot of sense since ultimately the point cloud is the result we are mostly interested in. On the flip side, it often leads to a burdensome manual search for the right fusion parameters in order to score well on the public benchmarks. In this work, we tackle the aforementioned problem with HAMMER, a Hierarchical And Memory-efficient MVSNet with Entropy-filtered Reconstructions. We propose to learn a filtering mask based on entropy, which, in combination with a simple two-view geometric verification, is sufficient to generate high quality 3D models of any input scene. Distinct from existing works, a tedious manual parameter search for the fusion step is not required. Furthermore, we take several precautions to keep the memory requirements for our method very low in the training as well as in the inference phase. Our method only requires 6 GB of GPU memory during training, while 3.6 GB are enough to process 1920×1024 images during inference. Experiments show that HAMMER ranks amongst the top published methods on the DTU and Tanks and Temples benchmarks in the official metrics, especially when keeping the fusion parameters fixed.

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Action-By-Detection: Efficient Forklift Action Detection for Autonomous Mobile Robots in Warehouses

Action-By-Detection: Efficient Forklift Action Detection for...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Alexander Prutsch Horst Possegger Horst Bischof Institute of Computer Graphics and Vision Graz University of Technology

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Understanding actions of other agents increases the efficiency of autonomous mobile robots (AMRs) since they encompass intention and indicate future movements. We propose a new method that allows us to infer vehicle actions using a shallow image-based classification model. The actions are classified via bird’s-eye view scene crops, where we project the detections of a 3D object detection model onto a context map. We learn map context information and aggregate temporal sequence information without requiring object tracking. This results in a highly efficient classification model that can easily be deployed on embedded AMR hardware. To evaluate our approach, we create new large-scale synthetic datasets showing warehouse traffic based on real vehicle models and geometry.

关键词： Point cloud compression Solid modeling Three-dimensional displays Pipelines Detectors Predictive models Mobile robots

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