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Towards Fast Face Image Quality Assessment via Latent Diffusion Model 18th

Towards Fast Face Image Quality Assessment via Latent Diffu...

引用

18th Chinese conference on Biometric Recognition, CCBR 2024

作者： Yan, Zheyu Zhao, Weisong Zhu, Xiangyu Gao, Li Zhang, Xiao-Yu Lei, Zhen State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China China Mobile Financial Technology Co. Ltd. Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Centre for Artificial Intelligence and Robotics Hong Kong Institute of Science and Innovation Chinese Academy of Sciences Hong Kong Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9789819610679

Face recognition technology has gradually come into wide application in real life. However, in real-life unconstrained scenarios, low-quality images could be matched to unpredictable individuals, thereby affecting the stability of recognition and even causing security problems. Conducting quality assessment on face images before facial recognition can help alleviate these problems by rejecting low-quality samples. In this paper, we propose a Fast Diffusion-model-based Face Image Quality Assessment method (FDif-FIQA). It uses a diffusion model to perturb face images and measures image quality based on the similarity of features before and after the perturbation. The denoising process is transferred from the pixel space to the latent space, thereby reducing the computational cost. We evaluated the performance of this method with multiple mainstream face recognition models on various public datasets. Compared to state-of-the-art methods, our approach demonstrates highly competitive performance and significant improvements in computational efficiency. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Face recognition

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Prediction and Identification of Multiple Sclerosis Patients from Gait Features Using Machine Learning Algorithms and Ensemble methods in Embedded Sensors Environment 2

Prediction and Identification of Multiple Sclerosis Patients...

引用

2nd international conference on Computer Vision and Internet of Things, ICCVIoT 2024

作者： Rupani, Daksh Pal, Granth Soni, Kushagra Kumar, Suraj Kishoreraja, P.C. Sivakumar, R. Tamilnadu Vellore632014 India Tamilnadu Vellore632014 India Tamilnadu Vellore632014 India Tamilnadu Vellore632014 India

ISBN: (纸本)9781837242672

Multiple sclerosis is a chronic autoimmune disease prevalent among mid-aged to younger adults. The objectives of implementing advanced computational techniques in gait feature analysis are to refine the analytical process, improve the accuracy of gait assessment and understanding human locomotion patterns to predict and identify the multiple sclerosis patient using logistic regression, Support Vector Machine and K-Nearest Neighbours classifiers. We aim to automate the analysis of gait feature data collected from various sensors to expedite the prediction and identification of multiple sclerosis. This automation facilitates the early detection of subtle deviations in gait dynamics, which could signify neurological impairments. Logistic regression, Support Vector Machine and KNN models are capable of processing intricate gait data to discern meaningful patterns and characteristics indicative of multiple sclerosis conditions with minimum number of gait features. This paper introduces new gait features to improve the model's performance and differentiate the multiple sclerosis from healthy control group. KNN demonstrates superior accuracy (96.55%) compared to Support Vector Machine (90.05%) and logistic regression (86.35%). KNN with bagging achieved the highest prediction accuracy (94.4%), while logistic regression with bagging had the lowest (83.3%). This paper proposes an ensemble learning method that uses weight averaging to get combine the predictions of above models with ensemble accuracy of 90.12%. A new index called match index is created for the identification of multiple sclerosis. © The Institution of Engineering & Technology 2024.

关键词： Logistic regression

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Robust Collaborative Perception against Temporal Information Disturbance

Robust Collaborative Perception against Temporal Information...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Xunjie He Yiming Li Te Cui Meiling Wang Tong Liu Yufeng Yue School of Automation Beijing Institute of Technology Beijing China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Collaborative perception facilitates a more comprehensive representation of the environment by leveraging complementary information shared among various agents and sensors. However, practical applications often encounter information disturbance which includes perception packet loss and time delays, and a comprehensive framework that can simultaneously address such issues is absent. In addition, the feature extraction process prior to fusion is not sufficient, as it lacks exploration of the local semantics and context dependencies of individual features. To enhance both accuracy and robustness, this paper introduces a novel framework named Robust Collaborative Perception against Temporal Information Disturbance, which predicts perception information when disturbance occurs. Specifically, the Historical Frame Prediction (HFP) module is introduced to make compensation for information loss with temporal association excavation of historical features. Based on the predicted features generated by the HFP module, the Pyramid Attention Integration (PAI) module is introduced to augment local semantics and incorporate global long-range dependencies through multi-scale window attention. Compared with existing methods on the publicly available dataset OPV2V, our approach exhibits superior performance and expanded robustness in the 3D object detection task. The code will be publicly available at https://***/hexunjie/Ro-temd.

关键词： Three-dimensional displays Accuracy Delay effects Semantics Collaboration Packet loss Object detection

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Towards the automation of Wildfire Monitoring with Aerial Vehicles: The FIREFRONT Project 26th

Towards the Automation of Wildfire Monitoring with Aerial...

引用

26th international conference on Pattern Recognition, ICPR 2022

作者： Ribeiro, Ricardo Bernardino, Alexandre Cruz, Gonçalo Silva, Diogo Felix, Luís Caetano, João Folgado, Duarte Francisco, João Simões, Nuno Viegas, Carlos Xavier Viegas, Domingos Xavier Harkat, Houda Nascimento, Jose Institute for Systems and Robotics Instituto Superior Técnico Lisbon Portugal CIAFA FAP Sintra Portugal AeroEspaço Science Center - ACTV Torres Vedras Portugal UAVision Torres-Vedras Portugal ADAI Univ Coimbra Coimbra Portugal UNINOVA-CTS Monte Caparica Portugal IT Lisboa Portugal

ISBN: (纸本)9783031377419

This paper describes an integrated system for wildfire monitoring with aerial vehicles. The system is composed of an airborne payload compatible with UAVs and manned aircraft and associated communication and software infrastructure. The system is able to detect fire and smoke in the payload thermal and RGB images, georeference their location in ground coordinates using telemetry (GPS, IMU), and forecast the fire front spread. We describe the progress made in the payload system, acquired datasets, fire/smoke segmentation methods, fire georeferencing, and wildfire spread modeling, providing the reader with a overview of the project and related technical publications. © 2023, Springer Nature Switzerland AG.

关键词： Fires

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Generating Spatial Binaural Environmental Audio Alert on a Personal Audio Device

Generating Spatial Binaural Environmental Audio Alert on a P...

引用

robotics, automation and Artificial Intelligence (RAAI), IEEE international conference on

作者： Chirag Garg Software Engineering Samsung Electronics Noida India

ISBN: (数字)9798331520038

ISBN: (纸本)9798331520045

In this paper, we address the audio spatial alert, which aims to provide spatial binaural environmental audio alert on a personal audio device. Existing methods have achieved binaural sound reproduction in head mounted devices and relates to audio spatialization technique, but fails to teach the ability to determine the direction of environmental audio event producing source from the surrounding and generating a binaural sound alert by a virtual sound source in respect to current head direction of the user so that for that instant user can understand from which direction environmental sound is coming. To address these issues, we propose a method for providing spatial binaural environmental audio alert on a personal audio device. The proposed solution determines head direction and activity of a user in an environment in a time frame and, further includes capturing one or more audio events occurring in the environment in the time frame. This also includes Direction Estimation Model, which helps in determining direction of environmental audio event producing source. Hence, proposed PBAG (Personalized Binaural Alert Generator) localizes a virtual sound source for generating a spatial binaural audio alert with respect to current head direction of the user and providing the alert on the personal audio device.

关键词： Location awareness automation Accuracy Estimation Feature extraction Real-time systems Generators Calibration Artificial intelligence Robots

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Nonlinear Dynamical System Identification Under External Disturbances by Maximum a Posteriori (MAP) Estimation for robotics

Nonlinear Dynamical System Identification Under External Dis...

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18th IEEE international conference on Advanced Motion Control, AMC 2024

作者： Yanabe, Daisuke Kanoga, Suguru Fremont, Vincent Murakami, Toshiyuki Graduate School of Science and Technology Keio University Kanagawa Japan Tokyo Japan Ecole Centrale de Nantes Nantes Universite and Cnrs LS2N Nantes France Keio University Department of System Design Engineering Kanagawa Japan

ISBN: (纸本)9798350342819

In nonlinear dynamical system identification, a flexible statistical model is commonly used for identification to compensate for non-linear elements. The flexibility in the statistical model induces a high sensitivity to the existence of external disturbances that could deteriorate the identification accuracy substantially. Most of the identification techniques assume a processing noise as a statistical variable and apply a maximum likelihood-based method to identify the dynamical system. Most of these methods do not take into account the effect of external disturbances. In this work, we also considered external disturbances as one of the statistical parameters to estimate. By estimating values for external disturbances and applying maximum a posteriori estimation, identification accuracy could be improved compared to models without considering external disturbances. © 2024 IEEE.

关键词： Nonlinear dynamical systems

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End-to-end Object Detection System Using Multi-Source Data Fusion for Autonomous Driving

End-to-end Object Detection System Using Multi-Source Data F...

引用

robotics, Control and automation Engineering (RCAE), international conference on

作者： Yuchen Zha Li Guo Yajie Chen Yue Wu Jiabao Wang Runze Li School of Electrical Engineering Anhui Polytechnic University Wuhu China School of Automation Nanjing University of Aeronautics and Astronautics Nanjing China

ISBN: (数字)9798350355642

ISBN: (纸本)9798350355659

With the rapid development of autonomous driving, modern vehicles are equipped with multiple sensors. Data from these various sensors assist and cross-reference each other, enhancing autonomous driving capabilities. However, the efficient and cost-effective utilization of the vast amounts of data generated by these sensors remains a significant challenge. This study presents an end-to-end multi-source data object detection system based on the YOLOv8 model. By introducing the Pixel Cluster Rotation (PCR) and hint layer, the system enables the fusion of multi-source data without significantly increasing resource requirements. Evaluations show that compared to vision-only object detection, these methods allow the fusion of LiDAR and RGB data with approximately 0.38% additional neural network parameters, resulting in a 2.9% increase in the system's mean Average Precision (mAP). This research can be applied to low-cost platforms with limited system resources, promoting the broader adoption of autonomous driving technology.

关键词： YOLO Laser radar System performance Neural networks Sensor fusion Robot sensing systems Sensor systems Sensors Reliability Autonomous vehicles

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Time-Varying Multi-Goal Path Planning with Multi-Tree RRT* Algorithm for Quadruped Robots

Time-Varying Multi-Goal Path Planning with Multi-Tree RRT* A...

引用

international conference on Control, automation, robotics and Vision (ICARCV)

作者： Haidong Wang Xiaogang Xiong Haixiang Zhou Ke Li Yunjiang Lou School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen Shenzhen China Beijing Research Institute of Precise Mechatronics and Controls Beijing China

ISBN: (数字)9798331518493

ISBN: (纸本)9798331518509

Quadruped robots are being widely deployed in various scenarios with uneven terrains, such as rescue and supervision, due to their ability to climb obstacles and carry heavy loads. However, these robots struggle when faced with complex tasks that require reaching time-varying multiple target locations or landmarks. The visiting order of the landmarks and the total travel cost can significantly impact their overall work efficiency. The situation is worsened by limited on-board computing resources, restricted battery storage, and real-time computing demand for navigation systems. To address this issue, we propose a novel approach of multi-goal path planning specifically designed for quadruped robots. Our system extends the conventional Rapidly-Exploring Random Tree (RRT) algorithm to optimize the visiting order of multi-goal while taking into account the kinematics and safety-distance of quadruped robots. Simulation and experimental results demonstrate that our proposed multi-goal path planning system is more efficient than traditional methods in navigating complex tasks while reaching each sub-target position.

关键词： Costs automation Navigation Virtual links Merging Kinematics Path planning Real-time systems Batteries Quadrupedal robots

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Towards Safe Motion Planning in Human Workspaces: A Robust Multi-agent Approach

Towards Safe Motion Planning in Human Workspaces: A Robust M...

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IEEE international conference on robotics and automation (ICRA)

作者： Lo, Shih-Yun Fernandez, Benito Stone, Peter Thomaz, Andrea L. Univ Texas Austin Austin TX 78712 USA Sony AI Tokyo Japan

ISBN: (纸本)9781728190778

It is becoming increasingly feasible for robots to share a workspace with humans. However, for them to do so safely while maintaining agile performance, they need the ability to smoothly handle the dynamics and uncertainty caused by human motions. Markov Decision Processes (MDPs) serve as a common framework to formulate robot planning problems. However, because of its single-agent formulation, such planner cannot account for human reaction when evaluating robot actions. The robot can thus suffer from unsafe motions and move in ways that are hard for nearby humans to understand. To resolve this, we instead model robot planning in human workspaces as a Stochastic Game, and contribute a robust planning algorithm, which enables the robot to account for its prediction errors in human responses to prevent collision, while not losing agility, opposed to traditional maximin optimization techniques, by applying maximin operation only at "critical states". We validate the approach under partial knowledge of pedestrian behaviors, and show that our approach encounters zero collision despite imperfect prediction, while improving path efficiency, compared to baselines.

关键词： Uncertainty Robot kinematics Dynamics Games Predictive models Markov processes Prediction algorithms

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Knowledge Refinement: An Interpretable Analytics for Travel Behaviors Based on Knowledge automation

Knowledge Refinement: An Interpretable Analytics for Travel ...

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international conference on Intelligent Transportation

作者： Peijun Ye Renrui Zhang Shichao Ge State Key Laboratory of Multimodal Artificial Intelligence Systems Chinese Academy of Sciences Institute of Automation Beijing China School of Automation and Intelligence Beijing Jiaotong University Beijing China

ISBN: (数字)9798331505929

ISBN: (纸本)9798331505936

Travel behavioral analysis is crucial to the demand computation, which underpins traffic planning and optimization of transportation management and control. While traditional methods seek to descriptively learn the travel patterns through behavioral models as well as big data from multiple sources, they perform weak predictive ability of system's evolutionary features. To model and analyze the dynamics of such cyber physical social systems, this paper proposes an interpretable analytical method for travel behaviors based on knowledge automation (named as knowledge refinement). The new approach provides a feasible and reliable methodology for the analytics of heterogenous human participated systems, and is validated using one month's data of public transportation in Beijing. Results indicate that the approach not only has the state-of-the-art “reproductive” ability of actual traffic flows compare with traditional methods, but also can further reveal systemic dynamics with semantics such as the stable or divergent regions.

关键词： automation Computational modeling Semantics Predictive models Big Data Data models Planning Reliability Public transportation Optimization

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