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Leveraging Augmented Reality,Semantic-Segmentation,and VANETs for Enhanced Driver’s Safety Assistance

computers, Materials & Continua 2024年第1期78卷 1443-1460页

作者： Sitara Afzal Imran Ullah Khan Irfan Mehmood Jong Weon Lee Mixed Reality and Interaction Lab Department of SoftwareSejong UniversitySeoul05006Korea Faculty of Engineering and Digital Technologies School of Computer ScienceAI and ElectronicsUniversity of BradfordBradfordUK

Overtaking is a crucial maneuver in road transportation that requires a clear view of the road ***,limited visibility of ahead vehicles can often make it challenging for drivers to assess the safety of overtaking maneuvers,leading to accidents and *** this paper,we consider atrous convolution,a powerful tool for explicitly adjusting the field-of-view of a filter as well as controlling the resolution of feature responses generated by Deep Convolutional Neural Networks in the context of semantic image *** article explores the potential of seeing-through vehicles as a solution to enhance overtaking ***-through vehicles leverage advanced technologies such as cameras,sensors,and displays to provide drivers with a real-time view of the vehicle ahead,including the areas hidden from their direct line of *** address the problems of safe passing and occlusion by huge vehicles,we designed a see-through vehicle system in this study,we employed a windshield display in the back car together with cameras in both *** server within the back car was used to segment the car,and the segmented portion of the car displayed the video from the front *** see-through system improves the driver’s field of vision and helps him change lanes,cross a large car that is blocking their view,and safely overtake other *** network was trained and tested on the Cityscape dataset using semantic *** transparent technique will instruct the driver on the concealed traffic situation that the front vehicle has *** our findings,we have achieved 97.1% *** article also discusses the challenges and opportunities of implementing see-through vehicles in real-world scenarios,including technical,regulatory,and user acceptance factors.

关键词： Overtaking safety augmented reality VANET V2V deep learning

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AI-Driven Relocation Tracking in Dynamic Kitchen Environments 14

AI-Driven Relocation Tracking in Dynamic Kitchen Environment...

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14th International Conference on computer and Knowledge Engineering, ICCKE 2024

作者： Esfahani, Arash Nasr Hosseini, Hamed Masouleh, Mehdi Tale Kalhor, Ahmad Sajedi, Hedieh Human and Robot Interaction Lab School of Electrical and Computer Engineering University of Tehran Tehran Iran School of Mathematics Statistics and Computer Science University of Tehran Tehran Iran

ISBN: (纸本)9798331511272

As smart homes become more prevalent in daily life, the ability to understand dynamic environments is essential which is increasingly dependent on AI systems. This study focuses on developing an intelligent algorithm which can navigate a robot through a kitchen, recognizing objects, and tracking their relocation. The kitchen was chosen as the testing ground due to its dynamic nature as objects are frequently moved, rearranged and replaced. Various techniques, such as SLAM feature-based tracking and deep learning-based object detection (e.g., Faster R-CNN), are commonly used for object tracking. Additionally, methods such as optical flow analysis and 3D reconstruction have also been used to track the relocation of objects. These approaches often face challenges when it comes to problems such as lighting variations and partial occlusions, where parts of the object are hidden in some frames but visible in others. The proposed method in this study leverages the YOLOv5 architecture, initialized with pre-trained weights and subsequently fine-tuned on a custom dataset. A novel method was developed, introducing a frame-scoring algorithm which calculates a score for each object based on its location and features within all frames. This scoring approach helps to identify changes by determining the best-associated frame for each object and comparing the results in each scene, overcoming limitations seen in other methods while maintaining simplicity in design. The experimental results demonstrate an accuracy of 97.72%, a precision of 95.83% and a recall of 96.84% for this algorithm, which highlights the efficacy of the model in detecting spatial changes. ©2024 IEEE.

关键词： Optical flows

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Dense 3D Gaussian Splatting Initialization for Sparse Image Data

Dense 3D Gaussian Splatting Initialization for Sparse Image ...

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45th Annual Conference on European Association for computer Graphics, EUROGRAPHICS 2024

作者： Seibt, S. Chang, T. von Rymon Lipinski, B. Latoschik, M.E. Game Tech Lab. Faculty of Computer Science Nuremberg Institute of Technology Nuremberg Germany Human-Computer Interaction Group Institute of Computer Science University of Wuerzburg Wuerzburg Germany

ISBN: (纸本)9783038682394

This paper presents advancements in novel-view synthesis with 3D Gaussian Splatting (3DGS) using a dense and accurate SfM point cloud initialization approach. We address the challenge of achieving photorealistic renderings from sparse image data, where basic 3DGS training may result in suboptimal convergence, thus leading to visual artifacts. The proposed method enhances precision and density of initially reconstructed point clouds by refining 3D positions and extrapolating additional points, even for difficult image regions, e.g. with repeating patterns and suboptimal visual coverage. Our contributions focus on improving "Dense Feature Matching for Structure-from-Motion" (DFM4SfM) based on a homographic decomposition of the image space to support 3DGS training: First, a grid-based feature detection method is introduced for DFM4SfM to ensure a well-distributed 3D Gaussian initialization uniformly over all depth planes. Second, the SfM feature matching is complemented by a geometric plausibility check, priming the homography estimation and thereby improving the initial placement of 3D Gaussians. Experimental results on the NeRF-LLFF dataset demonstrate that this approach achieves superior qualitative and quantitative results, even for fewer views, and the potential for a significantly accelerated 3DGS training with faster convergence. © 2024 The Authors.

关键词： Gaussian distribution

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DFM4SFM - Dense Feature Matching for Structure from Motion

DFM4SFM - Dense Feature Matching for Structure from Motion

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2023 IEEE International Conference on Image Processing Challenges and Workshops, ICIPCW 2023

作者： Seibt, Simon Von Rymon Lipinski, Bartosz Chang, Thomas Latoschik, Marc Erich Nuremberg Institute of Technology Game Tech Lab Faculty of Computer Science Nuremberg Germany Institute of Computer Science University of Wuerzburg Human-Computer Interaction Group Wuerzburg Germany

ISBN: (纸本)9798350302585

Structure from motion (SfM) is a fundamental task in computer vision and allows recovering the 3D structure of a stationary scene from an image set. Finding robust and accurate feature matches plays a crucial role in the early stages of SfM. So in this work, we propose a novel method for computing image correspondences based on dense feature matching (DFM) using homographic decomposition: The underlying pipeline provides refinement of existing matches through iterative rematching, detection of occlusions and extrapolation of additional matches in critical image areas between image pairs. Our main contributions are improvements of DFM specifically for SfM, resulting in global refinement and global extrapolation of image correspondences between related views. Furthermore, we propose an iterative version of the Delaunay-triangulation-based outlier detection algorithm for robust processing of repeated image patterns. Through experiments, we demonstrate that the proposed method significantlv improves the reconstruction accuracy. © 2023 IEEE.

关键词： computer vision

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A 2D Geometry Based Grasping Pose Generation Algorithm for a Two-Finger Robot Hand 31

A 2D Geometry Based Grasping Pose Generation Algorithm for a...

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31st International Conference on Electrical Engineering, ICEE 2023

作者： Akbari, Arash Akbari, Arman Masouleh, Mehdi Tale University of Tehran Department of Computer Science Tehran Iran School of Electrical and Computer Engineering Human and Robot Interaction Laboratory Tehran Iran

ISBN: (纸本)9798350312560

Robot grasping is of paramount importance in industrial and service robotics. In recent years, various data-driven algorithms have been proposed to solve the problem of grasp detection and a part of them are based on reinforcement learning (RL) approaches. In a variety of proposed algorithms, random key points are being employed which will make the learning process inefficient and time-consuming. In this paper, a geometry-based algorithm is presented which can find grasp poses based on the geometry of the unknown object and propose the ones which may lead to successful grasping. For the grasp contacts computation part, the presented algorithm produces a finite number of key points based on the 2D shape of the object from a specific point of view. Afterward, it will narrow down the candidate points and output a finite number of successful grasp poses based on three grasp quality metrics for various unknown objects. Three approaches are proposed in order to achieve center points which can describe different parts of a 2D shape. Then, the obtained points are used as the center of circles which are tangent to the 2D shape contour. Also, a new grasp quality metric is proposed. The time of the grasp and the amount of object disorientation after grasping are considered as a metric to evaluate the successfulness of the grasp. Simulation results demonstrate that the proposed algorithm for unknown object grasping can find a finite number of successful grasp poses for different seen or unseen objects without using any random point © 2023 IEEE.

关键词： Geometry

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Enhancing Immersive Sensemaking with Gaze-Driven Recommendation Cues 25

Enhancing Immersive Sensemaking with Gaze-Driven Recommendat...

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30th International Conference on Intelligent User Interfaces, IUI 2025

作者： Tahmid, Ibrahim Asadullah North, Chris Davidson, Kylie Whitley, Kirsten Bowman, Doug Computer Science Virginia Tech BlacksburgVA United States Department of Computer Science Virginia Tech BlacksburgVA United States Center for Human-Computer Interaction Virginia Tech BlacksburgVA United States Department of Defense College ParkMD United States

ISBN: (纸本)9798400713064

Sensemaking is a complex task that places a heavy cognitive demand on individuals. With the recent surge in data availability, making sense of vast amounts of information has become a significant challenge for many professionals, such as intelligence analysts. Immersive technologies such as mixed reality offer a potential solution by providing virtually unlimited space to organize data. However, the difficulty of processing, filtering relevant information, and synthesizing insights remains. We proposed using eye-tracking data from mixed reality head-worn displays to derive the analyst's perceived interest in documents and words, and convey that part of the mental model to the analyst. The global interest of the documents is reflected in their color, and their order on the list, while the local interest of the documents is used to generate focused recommendations for a document. To evaluate these recommendation cues, we conducted a user study with two conditions: a gaze-aware system, EyeST, and a "Freestyle"system without gaze-based visual cues. Our findings reveal that the EyeST helped analysts stay on track by reading more essential information while avoiding distractions. However, this came at the cost of reduced focused attention and perceived system performance. The results of our study highlight the need for explainable AI in human-AI collaborative sensemaking to build user trust and encourage the integration of AI outputs into the immersive sensemaking process. Based on our findings, we offer a set of guidelines for designing gaze-driven recommendation cues in an immersive environment. © 2025 Copyright held by the owner/author(s).

关键词： Information filtering

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Rehearsal Reality: Exploring the Visualization of Experts’ Mental Rehearsals from a First-Person Perspective to Support the Motor Learning of Novices

Rehearsal Reality: Exploring the Visualization of Experts’ ...

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2025 CHI Conference on human Factors in Computing Systems, CHI EA 2025

作者： Suzuki, Yuto Sakamoto, Daisuke Ono, Tetsuo Human-Computer Interaction Lab. Hokkaido University Sapporo Japan Division of Computer Science and Information Technology Hokkaido University Sapporo Japan

ISBN: (纸本)9798400713958

Understanding the cognitive aspects of motor learning is crucial for novices to enhance learning and performance. One approach is mental rehearsal—"the cognitive rehearsal of a task prior to performance"—which pre-activates neural pathways and fosters deeper cognitive understanding. Although highly efficient, mental rehearsal can be challenging for novices, who often struggle to form vivid imagery of unfamiliar skills. We propose a concept of rehearsal reality, an XR-based application that overlays the mental rehearsals of experts onto real practice environment. We developed rehearsal reality for golf putting based on expert golfers’ pre-putt mental imagery and conducted a qualitative study with 36 novice participants to understand novices’ subjective learning experiences. The results suggest that visualizing the mental rehearsals of experts can enhance the motor skills and external cognitive understanding of novices in golf putting. This finding can guide the potential of the mental rehearsals of experts as visual cues for motor learning. © 2025 Copyright held by the owner/author(s).

关键词： Visualization

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Segmentation and Immersive Visualization of Brain Lesions Using Deep Learning and Virtual Reality 19

Segmentation and Immersive Visualization of Brain Lesions Us...

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19th ACM SIGGRAPH International Conference on Virtual-Reality Continuum and its Applications in Industry, VRCAI 2024

作者： Kelley, Brendan Plabst, Lucas Plabst, Lena Natural User Interaction Lab Colorado State University Fort CollinsCO United States Department of Human-Computer Interaction Julius Maximilian University of Würzburg Würzburg Germany

ISBN: (纸本)9798400713484

Magnetic resonance imaging (MRIs) are commonly used for diagnosing potential neurological disorders, however preparation and interpretation of MRI scans requires professional oversight. Additionally, MRIs are typically viewed as single cross sections of the affected regions which does not always capture the full picture of brain lesions and can be difficult to understand due to 2D's inherent abstraction of our 3D world. To address these challenges we propose a immersive visualization pipeline that combines deep learning image segmentation techniques using a VGG-16 model trained on MRI fluid attenuated inversion recovery (FLAIR) with virtual reality (VR) immersive analytics. Our visualization pipeline begins with our VGG-16 model predicting which regions of the brain are potentially affected by a disease. This output, along with the original scan, are then volumentrically rendered. These renders can then be viewed in VR using an head mounted display (HMD). Within the HMD users can move through the volumentric renderings to view the affected regions and utilize planes to view cross sections of the MRI scans. Our work provides a potential pipeline and tool for diagnosis and care. © 2024 ACM.

关键词： Image segmentation

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What Lies Beneath? Exploring the Impact of Underlying AI Model Updates in AI-Infused Systems 25

What Lies Beneath? Exploring the Impact of Underlying AI Mod...

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2025 CHI Conference on human Factors in Computing Systems, CHI 2025

作者： Mohanty, Vikram Lim, Jude Luther, Kurt Human-Computer Interaction Institute Carnegie Mellon University PittsburghPA United States Independent Researcher ArlingtonVA United States Department of Computer Science Center for Human-Computer Interaction Virginia Tech AlexandriaVA United States

ISBN: (纸本)9798400713941

AI models are constantly evolving, with new versions released frequently. human-AI interaction guidelines encourage notifying users about changes in model capabilities, ideally supported by thorough benchmarking. However, as AI systems integrate into domain-specific workflows, exhaustive benchmarking can become impractical, often resulting in silent or minimally communicated updates. This raises critical questions: Can users notice these updates? What cues do they rely on to distinguish between models? How do such changes affect their behavior and task performance? We address these questions through two studies in the context of facial recognition for historical photo identification: an online experiment examining users' ability to detect model updates, followed by a diary study exploring perceptions in a real-world deployment. Our findings highlight challenges in noticing AI model updates, their impact on downstream user behavior and performance, and how they lead users to develop divergent folk theories. Drawing on these insights, we discuss strategies for effectively communicating model updates in AI-infused systems. © 2025 Copyright held by the owner/author(s).

关键词： AI Model Updates

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WaterHCI: Water in human-computer interaction

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Foundations and Trends in human-computer interaction 2024年第4期18卷 338-412页

作者： Vega, Maria Montoya Smith, Ian Clashing, Christal Patibanda, Rakesh Ananthanarayan, Swamy Pell, Sarah Jane Mueller, Florian Exertion Games Lab Department of Human-Centred Computing Monash University Australia Faculty of Computer Science University of New Brunswick Canada Department of Human-Centred Computing Monash University Australia

Over recent years, there has been an increase in the coming together of interactive technology and water, leading to the emergence of WaterHCI, a distinct subfield of human-computer interaction (HCI). However, there is little work that aims to paint a comprehensive picture of the work around WaterHCI experiences so far, limiting the opportunity to identify directions for future research. This monograph aims to address this through an articulation of prior WaterHCI works structured using two frameworks that aim to offer a better understanding of the design of aquatic experiences through four key user experiences across six different degrees of contact with water. This articulation allows us to highlight underexplored areas that could guide WaterHCI researchers in identifying what to research next in order to bring the field forward as a whole. Ultimately, our work aims to help so that more people can profit from the many benefits that combining interactive technology and water affords. ©2024 M. M. Vega et al.

关键词： aqua aquatic interfaces Water

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