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Few-shot tire tracks recognition based on metric learning

Engineering Applications of Artificial Intelligence 2025年 156卷

作者： Zhai, Longlong He, Jinrong Li, Zhaokui Bi, Yingzhou College of Mathematics and Computer Science Yan'an University Yan'an716000 China Guangxi Key Lab of Human-machine Interaction and Intelligent Decision Nanning Normal University Nanning530100 China School of Computer Shenyang Aerospace University Shenyang110136 China

Tire tracks are crucial evidence for investigating and identifying traffic accident scenes. Intelligent recognition of tire track images enables rapid and precise identification of suspicious vehicles, assisting investigators in determining accident causes and liabilities. This enhances both the efficiency and accuracy of case resolution. However, tire track images are complex and diverse, significantly influenced by environmental factors such as lighting and road conditions. Moreover, the limited availability of tire track image samples poses a challenge for training deep learning models in practical applications. To address these challenges, particularly the limited generalization ability of few-shot metric learning models on unseen categories and their poor recognition rates in open-world environments, we propose a novel framework called TireNet for few-shot tire track recognition. First, we adopt a residual network integrated with coordinate attention as the backbone for feature extraction. Secondly, the context-aware features of the support set and the query set are extracted separately through attention-based bi-directional long short-term memory model. Subsequently, the cosine similarity between the features of the support set and the query set is calculated to determine the category of the query image. Finally, to address the class imbalance issue in the tire track image dataset, we utilize an improved Focal Loss for gradient updates. This enables the model to focus more on difficult samples during training, thereby enhancing the training efficiency, particularly in scenarios involving few-shot learning and open-world environments. To validate the proposed method, we constructed a tire track image dataset of 1700 samples, covering various environmental conditions, providing a rich resource for tire track recognition research. Experimental results demonstrate that the proposed method significantly outperforms few-shot image classification and classic machine

关键词： Deep learning

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Acquisition evaluation on outdoor scanning for archaeological artifact digitalization 15

Acquisition evaluation on outdoor scanning for archaeologica...

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15th International Joint Conference on computer vision, Imaging and computer Graphics Theory and Applications, VISIGRAPP 2020

作者： Kusuma Frisky, Aufaclav Zatu Fajri, Adieyatna Brenner, Simon Sablatnig, Robert Computer Vision Lab Institute of Visual Computing and Human-Centered Technology Faculty of Informatics TU Wien Wien Austria Department of Computer Science and Electronics Universitas Gadjah Mada Yogyakarta Indonesia Department of Archaeology Universitas Gadjah Mada Yogyakarta Indonesia

ISBN: (纸本)9789897584022

Archaeological archives are important assets because they provide primary information resources for research, mainly digital archives that not degraded. Instead of directly visiting a site, an archaeologist can examine and manipulate the data without harming the real object. However, choosing an efficient scanning scheme with detailed results is a challenging task. In this work, we present new sculpture models obtained in three different ways and assess it in two comparison approaches: A quantitative and qualitative assessment. The quantitative comparison architecture provides a detailed assessment of three different scanning mechanisms in two stages: point cloud and mesh comparison. This evaluation is purposed to describe the differences between unmodified data. Finally, a qualitative evaluation is performed by an expert and practitioner to explain the difference based on four different produced models to help their needs in the real application. Copyright © 2020 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved.

关键词： Scanning

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Trajectory prediction model for crossing-based target selection

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Virtual Reality & Intelligent Hardware 2019年第3期1卷 330-340页

作者： Hao ZHANG Jin HUANG Feng TIAN Guozhong DAI Hongan WANG Beijing Key Lab of Human-Computer Interaction Institute of SoftwareChinese Academy of SciencesBeijing 100190China State Key Lab of Computer Science Institute of SoftwareChinese Academy of SciencesBeijing 100190China

Background Crossing-based target selection motion may attain less error rates and higher interactive speed in some *** of the research in target selection fields are focused on the analysis of the interaction ***,as trajectories play a much more important role in crossing-based target selection compared to the other interactive techniques,an ideal model for trajectories can help computer designers make predictions about interaction results during the process of target selection rather than at the end of the whole *** In this paper,a trajectory prediction model for crossing based target selection tasks is proposed by taking the reference of a dynamic model *** Simulation results demonstrate that our model performed well with regard to the prediction of trajectories,endpoints and hitting time for target-selection motion,and the average error of trajectories,endpoints and hitting time values were found to be 17.28%,2.73mm and 11.50%,respectively.

关键词： Target selection Crossing-based selection Trajectory prediction

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Algorithmic Mirror: Designing an Interactive Tool to Promote Self-Reflection for YouTube Recommendations

arXiv

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arXiv 2025年

作者： Kondo, Yui Dunnell, Kevin Xiao, Qing Zhao, Jun Rocher, Luc Oxford Internet Institute University of Oxford United Kingdom MIT Media Lab Massachusetts Institute of Technology United States Human-Computer Interaction Institute Carnegie Mellon University United States Department of Computer Science University of Oxford United Kingdom

Big Data analytics and Artificial Intelligence systems derive non-intuitive and often unverifiable inferences about individuals’ behaviors, preferences, and private lives. Drawing on diverse, feature-rich datasets of unpredictable value, these systems erode the intuitive connection between our actions and how we are perceived, diminishing control over our digital identities. While Explainable Artificial Intelligence scholars have attempted to explain the inner workings of algorithms, their visualizations frequently overwhelm end-users with complexity. This research introduces ‘hypothetical inference’—a novel approach that uses language models to simulate how algorithms might interpret users’ digital footprints and infer personal characteristics without requiring access to proprietary platform algorithms. Through empirical studies with fourteen adult participants, we identified three key design opportunities to foster critical algorithmic literacy: (1) reassembling scattered digital footprints into a unified map, (2) simulating algorithmic inference through LLM-generated interpretations, and (3) incorporating temporal dimensions to visualize evolving patterns. This research lays the groundwork for tools that can help users recognize the influence of data on platforms and develop greater autonomy in increasingly algorithm-mediated digital environments. Copyright © 2025, The Authors. All rights reserved.

关键词： Video analysis

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MLM-Net: Streamlined Multi-Lane Detection Network with Spatio-temporal Memory for Video Instance Lane Detection

MLM-Net: Streamlined Multi-Lane Detection Network with Spati...

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International Conference on Intelligent Transportation

作者： Xiaoqin Wang Yunfei Yin Faliang Huang Xianjian Bao School of Mechanical and Vehicle Engineering Chongqing University Chongqing P. R. China School of Computer Science Chongqing University Chongqing P. R. China Guangxi Key Lab of Human-machine Interaction and Intelligent Decision Nanning Normal University Nanning P. R. China School of Maharishi University of Management Fairfield IOWA USA

Lane detection is a crucial task in autonomous driving as it helps vehicles maintain stable and efficient driving in complex traffic environments. Image-based and video-based lane detection are two common methods, with video-based lane detection being more effective in handling complex scenarios such as lane occlusion and wear. However, most existing video-based lane detection methods suffer from information redundancy, where the same key frames of the same lane are repeatedly calculated during the detection of multiple lanes. Additionally, the spatio-temporal sequence information of video-based lanes is not fully utilized. To address these challenges, MLM-Net is proposed as a streamlined multilane network that facilitates the sharing of spatiotemporal information. The Global Memory Feature (GMF) module is redesigned to mitigate information redundancy across multiple lanes, while the Local Spatio-Temporal Feature (LSTF) module is introduced to enhance the utilization of spatiotemporal consistency information. Additionally, the Enhanced Global Memory Feature (EGMF) module is introduced to extract enhanced features and handle challenging detection scenarios. Experimental results demonstrate that our method achieves state-of-the-art performance for video instance lane detection on the VIL-100 dataset.

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A human-human Physical interaction System for Training Handwriting

A Human-Human Physical Interaction System for Training Handw...

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IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob)

作者： S. Buscaglione A. Noccaro C. Provenzale C. Bonsignori E. Ivanova L. Sparaci F. Taffoni E. Burdet D. Formica NEXT: Neurophysiology and Neuroengineering of Human-Technology Interaction Research Unit Università Campus Bio-Medico di Roma Italy Neurorobotics Lab Newcastle University Newcastle Upon Tyne UK Advanced Robotics and Human-Centred Technologies-CREO Laboratory Università Campus Bio-Medico di Roma Italy Department of Bioengineering Imperial College of Science Technology and Medicine UK School of Electric Engineering and Computer Science Queen Mary University of London UK Institute of Cognitive Sciences and Technologies (ISTC) National Research Council of Italy (CNR) Italy

ISBN: (数字)9798350386523

ISBN: (纸本)9798350386530

Handwriting training in childhood is known to influence academic success, foster self-esteem, and contribute to overall development. Previous studies underscored the positive impact of haptic feedback on handwriting learning using one-way communication systems that provided the trainee with haptic feedback based on pre-recorded trainer's movements. In this study, we propose a system involving bidirectional physical coupling (haptic communication) between a trainee and a trainer to exploit the advantages of haptic communication for motor task improvement, proved in tracking tasks. The system, composed of an interactive display and a coupling spring, was tested in a handwriting task performed by a dyad of healthy adults with different cursive handwriting backgrounds. The participants' performance demonstrated the feasibility of using this system for handwriting, opening the way for further studies to assess its benefits in handwriting improvement and learning.

关键词： Training Couplings Tracking Communication systems Biomechatronics Motors Haptic interfaces Springs Robots

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An Adaptive Bionic Sensor: Enhancing Ankle Joint Tracking with High Sensitivity and Superior Cushioning Performance

SSRN

引用

SSRN 2024年

作者： Jin, Jianqiao Zhang, Chen Zhao, Jianyuan Yu, Minghan Lei, Ming Jin, Chun Yin, Rui Zhao, Weiwei Sauvage Laboratory for Smart Materials The School of Integrated Circuit Harbin Institute of Technology Shenzhen518055 China Shenzhen Key Laboratory of Flexible Printed Electronics Technology Harbin Institute of Technology Shenzhen518055 China State Key Laboratory of Advanced Welding & Joining Harbin Institute of Technology Harbin150001 China Human-Computer Interaction Design Lab School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen518055 China

Flexible sensors are renowned for their rapid responsiveness, high flexibility, and outstanding mechanical properties, making them ideal for applications in wearable devices, sports and health monitoring, and human-machine interfaces. To enhance the sensing performance of these flexible sensors, researchers have drawn inspiration from nature, creating various bionic microstructures. However, these structures often manifest in the macroscopic form of thin films, which do little to improve impact resistance and joint protection. In response, this work achieves a highly sensitive and impact-resistant pressure sensor by integrating bionic principles at both micro and macro levels. Specifically, at the micro level, a muscle-like hydrogel system consisting of MXene nanosheets incorporated into the double network of polyvinyl alcohol and sodium alginate matrix is selected. At the macro level, the bionic prototypes of mimosa leaves and durian spikes with good energy absorption structure, together with octopus’ sucker with good adsorption capacity are selected, and the synergistic construction of the three structures is realized through 3D printing methods. The obtained sensor has an appropriate response range, sensitivity and stability. More importantly, the displacement deformation of the hydrogel can be reduced by 91.22%, while its adhesion strength can be increased by 57.5% compared to the unstructured hydrogel. As a proof-of-concept, the proposed bionic flexible sensor can be used to monitor the motion status of the human ankle joint in real-time, thereby assisting users in making real-time judgments on gait health. © 2024, The Authors. All rights reserved.

关键词： Sodium alginate

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Detection Thresholds for Vertical Gains in VR and Drone-based Telepresence Systems

Detection Thresholds for Vertical Gains in VR and Drone-base...

引用

IEEE Annual International Symposium Virtual Reality

作者： Keigo Matsumoto Eike Langbehn Takuji Narumi Frank Steinicke Cyber Interface Lab. The University of Tokyo Human-Computer Interaction University of Hamburg

ISBN: (数字)9781728156088

ISBN: (纸本)9781728156095

Several redirected walking techniques have been introduced and analyzed in recent years, while the main focus was on manipulations in horizontal directions, in particular, by means of curvature, rotation, and translation gains. However, less research has been conducted on the manipulation of vertical movements and its possible use as a redirection technique. Actually, vertical movements are fundamentally important, e.g., for remotely steering a drone using a virtual reality *** this paper, we explored vertical gains, a novel redirection technique, which enables us to purposefully manipulate the mapping of the user’s physical vertical movements to movements in the virtual space and the remote space. This approach allows natural and more active physical control of a real drone. To demonstrate the usability of vertical gains, we implemented a telepresence drone and vertical redirection techniques for stretching and crouching actions using common VR devices. We conducted two user studies to investigate the effective manipulation ranges and its usability: one study using a virtual environment (VE), and one using a camera stream from a telepresence drone. The results revealed that our technique could manipulate a users vertical movement without her/his noticing.

关键词： Drones Telepresence Legged locomotion Resists Virtual reality Aerospace electronics Three-dimensional displays

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Machine Learning and computer vision Techniques in Continuous Beehive Monitoring Applications: A Survey

arXiv

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arXiv 2022年

作者： Bilik, Simon Zemcik, Tomas Kratochvila, Lukas Ricanek, Dominik Richter, Miloslav Zambanini, Sebastian Horak, Karel Department of Control and Instrumentation Faculty of Electrical Engineering and Communication Brno University of Technology Technická 3058/10 Brno61600 Czech Republic Computer Vision and Pattern Recognition Laboratory Department of Computational Engineering Lappeenranta-Lahti University of Technology LUT Yliopistonkatu 34 Lappeenranta53850 Finland Computer Vision Lab Institute of Visual Computing & Human-Centered Technology Faculty of Informatics TU Wien Favoritenstr. 9/193-1 ViennaA-1040 Austria

Wide use and availability of machine learning and computer vision techniques allows development of relatively complex monitoring systems in many domains. Besides the traditional industrial domain, new applications appears also in biology and agriculture, where they may be used to detect infections, parasites and weeds, but also for automated monitoring and early warning systems. This goes in concordance with the introduction of the easily accessible hardware and development kits such as the Arduino, or RaspberryPi families. In this paper, we survey 50 papers focusing on the methods of automated beehive monitoring using computer vision techniques. Particularly on the pollen and Varroa mite detection together with the bee traffic monitoring. Such systems could also be used for monitoring of honeybee colonies and for the inspection of their health state, which could potentially identify dangerous states before the situation is critical, or to better plan periodic bee colony inspections and therefore save significant costs. Further on, we also include analysis of the research trends in this application field and we outline the possible directions of new development. Our paper is also aimed at veterinary and apidology professionals and experts, who may not be familiar with machine learning to introduce them to its capabilities, hence each family of techniques is prefaced by a brief theoretical introduction and motivation related to its base method. We hope that this paper will inspire other scientists to use machine learning techniques for other applications in beehive monitoring. © 2022, CC BY.

关键词： computer vision

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A New Dynamic Routing Network for Monocular Depth Estimation 12

A New Dynamic Routing Network for Monocular Depth Estimation

引用

12th International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, CYBER 2022

作者： Luo, Zhehao Luo, Sijin Liang, Guoyuan Wu, Xinyu Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Province Shenzhen China University of Chinese Academy of Sciences Beijing China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Chinese Academy of Sciences Guangdong Province Shenzhen China Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Province Shenzhen China

ISBN: (数字)9781665472678

ISBN: (纸本)9781665472678

Depth estimation is an essential task for understanding the geometry of 3D scenes. Compared with multi-view-based methods, monocular depth estimation is more challenging for the requirement of integrating not only global information but also local cues from various parts. Recently, numerous Convolutional Neural Networks (CNNs) based approaches relying on pre-defined static architectures have been reported. Although many of them have achieved remarkable improvements in accuracy, there remains problem with the generality of the static model when it comes to diverse scenes. In this paper, a novel deep network with dynamic routing architecture is proposed with a dynamically adjustable structure with respect to different inputs, therefore leading to not only improvements on both accuracy and generality but also decrease of the model parameters. Extensive experiments of monocular depth estimation have been conducted on two well-known challenging datasets (i.e., NYUD-v2 and Make3D) as well as the mixed dataset, and have verified the effectiveness of the proposed dynamic framework. © 2022 IEEE.

关键词： Deep neural networks

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