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检索条件"任意字段=Conference on Radar Sensor Technology and Data Visualization"

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RAV4D: A radar-Audia-Visual dataset for Indoor Multi - Person Tracking

RAV4D: A Radar-Audia-Visual Dataset for Indoor Multi - Perso...

引用

2024 IEEE radar conference, radarConf 2024

作者： Zhou, Yi Song, Ningfei Ma, Jieming Man, Ka Lok López-Benítez, Miguel Yu, Limin Yue, Yutao School of Advanced Technology Xi'an Jiaotong-Liverpool University Suzhou China University of Liverpool Department of Electrical Engineering and Electronics Liverpool United Kingdom Institute of Deep Perception Technology Jitri Wuxi China

ISBN: (纸本)9798350329209

Indoor multi-person tracking is a widely explored area of research. However, publicly available datasets are either oversimplified or provide only visual data. To fill this gap, our paper presents the RAV4D dataset, a novel multimodal dataset that includes data from radar, microphone arrays, and stereo cameras. This dataset is characterised by the provision of 3D positions, Euler angles and Doppler velocities. By integrating these different data types, RAV 4D aims to exploit the synergistic and complementary capabilities of these modalities to improve tracking performance. The development of RAV4D addresses two main challenges: sensor calibration and 3D annotation. A novel calibration target is designed to effectively calibrate the radar, stereo camera and microphone array. In addition, a visually guided annotation framework is proposed to address the challenge of annotating radar data. This framework uses head positions, heading orientation and depth information from stereo cameras and radar to establish accurate ground truth for multimodal tracking trajectories. The dataset is publicly available at https://***/records/l0208199. © 2024 IEEE.

关键词： Stereo image processing

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ROBUST JOINT ASSOCIATION AND REGISTRATION UNDER LARGE sensor BIAS

ROBUST JOINT ASSOCIATION AND REGISTRATION UNDER LARGE SENSOR...

引用

IET International radar conference 2023, IRC 2023

作者： Ni, Na Jiang, Qi Mao, Huafeng Zhang, Jichuan Wang, Rui Hu, Cheng Radar Research Lab School of Information and Electronics Beijing Institute of Technology Beijing China Advanced Technology Research Institute Beijing Institute of Technology Shandong Jinan China Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing China

ISBN: (纸本)9781839539954

radar observation of group targets has recently received much attention in the flight mechanism research and many other applications. Group targets are usually closely spaced with lots of missed detections and false alarms, making it difficult for spatial reconstruction. Multisensor systems make use of data from multiple radars to provide more accurate measurements and robust tracks than a single radar, playing an important role in the group targets observation. In the data fusion processes, multisensor measurements are associated after transformed into a global coordinate system. However, large sensor bias makes it difficult to associate measurements of closely spaced targets given outliers, and nonideal association further increase the sensor bias estimation error. In this paper, we proposed a robust joint association and registration method to simultaneously acquire sensor bias estimates and association results. A nonlinear least median of squares estimator is used to achieve better accuracy and robustness of sensor bias estimation. Simulation results show the better bias estimation and association performance of the proposed method compared to other algorithms. © The Institution of Engineering & technology 2023.

关键词： radar measurement

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Non-Contact Breath Rate Classification Using SVM Model and mmWave radar sensor data 2

Non-Contact Breath Rate Classification Using SVM Model and m...

引用

2nd International conference on Cyber Physical Systems, Power Electronics and Electric Vehicles, ICPEEV 2024

作者： Ali, Mohammad Wassaf Gupta, Ayushi Khan, Mujeev Wajid, Mohd Department of Computer Engineering Aligarh Muslim University Aligarh India Department of Electronics Engineering Aligarh Muslim University Aligarh India

ISBN: (纸本)9798350388664

This work presents the use of frequency-modulated continuous wave (FMCW) radar technology combined with a machine learning (ML) model to differentiate between Normal and Abnormal Breath Rates (BR). The recommended system non-contactly collects data (or signals) using FMCW radar, which depends on BR. Various Support Vector Machine (SVM) kernels are used to classify the observed data into Normal and Abnormal states. Prolonged experiments show good accuracy in BR classification, confirming the model's efficacy. The best accuracy is 95% with the smallest number of support vectors in the case of the quadratic polynomial kernel. © 2024 IEEE.

关键词： Polynomials

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MMA-Net: Multi-Modal Attention Network for 2-D Object Detection in Autonomous Driving

MMA-Net: Multi-Modal Attention Network for 2-D Object Detect...

引用

2025 IEEE International conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Gaur, Abhilash Goel, Shubh Goel, Kanishk Srirangarajan, Seshan Tseng, Po-Hsuan Feng, Kai-Ten New Delhi India Hsinchu Taiwan The Department of Electrical Engineering IIT Delhi New Delhi India The Department of Electrical Engineering Bharti School of Telecommunication Technology and Management IIT Delhi New Delhi India The Department of Electronic Engineering National Taipei University of Technology Taipei Taiwan The Department of Electronics and Electrical Engineering NYCU Hsinchu Taiwan

ISBN: (纸本)9798350368741

Autonomous driving technology relies heavily on sensor data for environment perception. Heterogeneous sensors such as lidar, radar, and camera have their own strengths and limitations. Therefore, relying on any single sensor would restrict the effectiveness of autonomous driving technology. However, integrating data from such heterogeneous sensors poses challenges due to differences in their representations. This article outlines a deep learning network aimed at designing modality-agnostic multi-modal fusion architecture. We study sensor data from different modalities and learn fine-grained representations using modality-specific feature encoders independently. Then, a multi-modal attention-based network (MMA-Net) is proposed to fuse the data from heterogeneous modalities. The proposed MMA-Net fuses multi-modal sensor data by jointly exploiting the inter-modality and intra-modality relationships among camera, lidar, and radar sensors. The effectiveness of the proposed multi-modal fusion architecture is demonstrated using 2-D object detection metrics through extensive experiments on a dataset generated using the CARLA simulator. © 2025 IEEE.

关键词： autonomous driving CARLA simulator multi-modal learning object detection sensor fusion

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Slope Movement and Saturation data Management System with data visualization 15

Slope Movement and Saturation Data Management System with Da...

引用

15th IEEE International conference on Humanoid, Nanotechnology, Information technology, Communication and Control, Environment, and Management, HNICEM 2023

作者： Caadyang, Mark Noe Christian P. Carlos, John Thomas Cuaresma, Dexter James L. Villegas, Marc Gregg M Austria, Yolanda D. Adamson University Computer Engineering Department Philippines Adamson University PCpE Computer Engineering Department Philippines

ISBN: (纸本)9798350381177

The purpose of the present study is to develop a Slope Movement and Saturation data Management System with data visualization. The design project was developed by making use of an integrated microcontroller technology that utilizes wireless transmission of data for its hardware. Web technologies were also used on the software part. The developed system was tested in a prepared test setup which is a scaled version of a slope. The researchers conclude that in order to achieve its primary objective, a network of slope monitoring probes must be fabricated to transmit sensor data wirelessly and a web application must be developed to visualize the stored sensor data. Among the recommendations of the researchers are to improve the hardware components for better results and to add a power consumption management feature. © 2023 IEEE.

关键词： data visualization

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Extrinsic Calibration Method for LiDAR and Millimeter-Wave radar Systems

Extrinsic Calibration Method for LiDAR and Millimeter-Wave R...

引用

2024 International conference on Advanced Control Systems and Automation Technologies, ACSAT 2024

作者： Yang, Yanping Ren, Mingwu Nanjing University of Science & Technology School of Computer Science and Engineering Nanjing China

ISBN: (纸本)9798331541668

Millimeter-wave radar, with its high frequency and short wavelength, is increasingly becoming a critical sensor in autonomous driving systems, where precise external calibration with lidar is essential for efficient sensor fusion. However, existing calibration algorithms for these two sensors are still limited. To address this, we propose an offline external calibration method based on a novel 3D calibration target that combines absorbing materials and corner reflectors. Using a point cloud segmentation network, we extract lidar target point clouds and utilize RCS information with clustering to accurately locate radar point clouds, achieving precise data extraction for different sensors. Additionally, we introduce a model coordinate system as a bridge to resolve the spatial relationship between lidar and radar, and enhance the ICP alignment algorithm by calculating an initial alignment matrix with minimal error straight-line alignment, allowing for coarse-to-fine optimization. This approach significantly improves ICP precision and efficiency, with experimental results in various scenarios validating the calibration accuracy and reliability of this method. © 2024 IEEE.

关键词： Calibration

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Analysis of Composite Human Actions Based on Millimeter-Wave radar 2

Analysis of Composite Human Actions Based on Millimeter-Wave...

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2nd IEEE International conference on Signal, Information and data Processing, ICSIDP 2024

作者： Wang, Xiaofeng Ma, Yongtao Zhao, Haibo Wang, Xiaolin Tianjin University School of Microelectronics Tianjin China

ISBN: (纸本)9798331515669

radar-based human sensing has emerged as a promising solution for non-contact human-computer intelligent interaction. Current existing works typically preset a set of actions and use deep learning algorithms to extract features for classification. These studies usually focus only on recognizing body activities (such as walking, standing, and sitting) or gestures (such as sliding, pushing, and rotating). When recognizing activities, hand movements are often ignored, while gesture recognition typically requires the body to remain still. However, people often make gestures during body activities to convey additional information. In this paper, we investigate the feasibility of using a millimeter-wave radar sensor to simultaneously recognize human activities and gestures when composite actions involving both body movements and gestures are present in the sensor's field of view. First, we establish the kinematic model of composite actions. Next, we analyze the Doppler-Range-Angle image features of these composite actions. Experimental results demonstrate that the sensor can simultaneously capture the motion information of both the body and the hand. Additionally, the motion characteristics of the body and hand show a certain degree of spatial distinction. © 2024 IEEE.

关键词： Gesture recognition

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Interpersonal Distance Tracking with mmWave radar and IMUs 22

Interpersonal Distance Tracking with mmWave Radar and IMUs

引用

22nd ACM/IEEE International conference on Information Processing in sensor Networks (IPSN)

作者： Dai, Yimin Shuai, Xian Tan, Rui Xing, Guoliang Nanyang Technol Univ Sch Comp Sci & Engn Singapore Singapore Chinese Univ Hong Kong Dept Informat Engn Hong Kong Peoples R China

ISBN: (纸本)9798400701184

Tracking interpersonal distances is essential for real-time social distancing management and ex-post contact tracing to prevent spreads of contagious diseases. Bluetooth neighbor discovery has been employed for such purposes in combating COVID-19, but does not provide satisfactory spatiotemporal resolutions. This paper presents ImmTrack, a system that uses a millimeter wave radar and exploits the inertial measurement data from user-carried smartphones or wearables to track interpersonal distances. By matching the movement traces reconstructed from the radar and inertial data, the pseudo identities of the inertial data can be transferred to the radar sensing results in the global coordinate system. The re-identified, radar-sensed movement trajectories are then used to track interpersonal distances. In a broader sense, ImmTrack is the first system that fuses data from millimeter wave radar and inertial measurement units for simultaneous user tracking and re-identification. Evaluation with up to 27 people in various indoor/outdoor environments shows ImmTrack's decimeters-seconds spatiotemporal accuracy in contact tracing, which is similar to that of the privacy-intrusive camera surveillance and significantly outperforms the Bluetooth neighbor discovery approach.

关键词： mmWave radar IMU association tracking

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Deviation Aware Mask for Localizing Ground Penetrating radar data Alignment 20

Deviation Aware Mask for Localizing Ground Penetrating Radar...

引用

20th International conference on Ground Penetrating radar, GPR, 2024

作者： Zhang, Pengyu Chen, Yuwei Shen, Liang Bi, Beizhen Jin, Tian Huang, Xiaotao College of Electronic Science and Engineering National University of Defense Technology Hunan Changsha410073 China Test Center National University of Defense Technology Xi'an710106 China

This paper proposes a data align pipeline for LGPR (Localization Ground Penetrating radar). LGPR achieves robot localization by matching prior underground data map, therefore high-precision maps are essential. Utilizing GPR in a time acquisition mode for robot localization enables the technology to generate maps with resolutions far superior to those produced by camera or lidar mapping methods. However, it also introduces several challenges. On one hand, the process of map generation heavily relies on velocity control of the moving platform, and non-uniform motion in the localization scenario can result in feature deformation of GPR images. On the other hand, in the absence of relative a priori position conditions, there exists a certain degree of spatial dimension offset between the multiple data acquisitions from array GPR systems, rendering existing time-series deformation correction schemes ineffective. To address these challenges, by designing a deviation-aware distance, we aim to enhance existing time-series regularization approaches without relying on additional sensor position information, thus achieving synchronized alignment of multiple rounds of ground-penetrating radar data for the generation of high-quality underground map data used to evaluate LGPR performance. In this work, we compare existing synchronization strategies of LGPR with those of other sensors and alignment schemes, and validate the effectiveness of our proposed approach. © Published under licence by IOP Publishing Ltd.

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ALGORITHMS AND ENVIRONMENT SENSING FOR AUTONOMOUS DRIVING technology 2

ALGORITHMS AND ENVIRONMENT SENSING FOR AUTONOMOUS DRIVING TE...

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2nd International conference on Mechatronic Automation and Electrical Engineering, ICMAEE 2024

作者： Wu, Jie Chong Qing University Chongqing400000 China

ISBN: (纸本)9781837242672

This paper reviews the current development and future trends of autonomous driving technology in recent years. In particular, the article focuses on three mainstream algorithms in the field of autonomous driving: mathematical modeling algorithms, multi-sensor fusion algorithms, and deep learning algorithms, as well as the corresponding environmental sensing technologies, including radar and cameras. This study thoroughly explores these algorithms and sensor technologies from the perspectives of technical content, existing challenges, and predicted future trends. The results of the study show that by combining multi-sensor data fusion with deep learning techniques, the environment-sensing capability of self-driving vehicles has been significantly improved. This enables the vehicle to accurately recognize its surroundings under various weather and lighting conditions, thereby significantly improving driving safety and efficiency. Although significant progress has been made in current environment sensing technologies, future research will need to continue to optimize algorithms and improve sensor performance in order to cope with more complex road conditions and ever-changing environments in order to achieve more advanced autonomous driving functions. © The Institution of Engineering & technology 2024.

关键词： data fusion

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