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Data fusion for Intelligent Crowd Monitoring and Management Systems: A Survey

IEEE ACCESS 2021年 9卷 47069-47083页

作者： Li, Xianzhi Yu, Qiao Alzahrani, Bander Barnawi, Ahmed Alhindi, Ahmed Alghazzawi, Daniyal Miao, Yiming Chinese Univ Hong Kong CUHK Dept Comp Sci & Engn Hong Kong Peoples R China Huazhong Univ Sci & Technol Sch Comp Sci & Technol Wuhan 430074 Peoples R China King Abdulaziz Univ Fac Comp & Informat Technol Jeddah Saudi Arabia Umm Al Qura Univ Dept Comp Sci Mecca Saudi Arabia

Intelligent Crowd Monitoring and Management Systems (ICMMSs) have become effective resources for strengthening safety and security along with enhancing early-warning capabilities to manage emergencies in crowded situations of smart cities and massive gatherings events. The main advantage of such systems is their ability to detect multiple features associated with the crowd gathering, as they enable multi-source sensors, multi-modal data, and powerful intelligent and analytical methods. Unlike traditional crowd monitoring systems, which make use of simplex forms of different data types, data and information associated with crowded scenarios can be collected, fused, processed and analyzed in large quantities for accurate global assessment and enhanced decision making processes in an ICMMS. Therefore, data fusion is introduced as an enabler to decrease data quantity, reduce data dimensions, and improve data quality. In this paper, we first survey the literature on data fusion application in crowd monitoring systems as we are developing a state-of-the-art ICMMS with data fusion as a major platform enabler. Next, we discuss some popular data fusion architectures and classifications from different perspectives. Based on this, we propose a multi-sensor, multi-modal, and dimensional ICMMS architecture based on data fusion. Then, we identify the data fusion processes in the ICMMS and classify them into sensor fusion, feature-based data fusion, and decision fusion. Relevant algorithms, applications and examples of three classes are elaborated. Finally, future data fusion research directions are discussed.

关键词： Data integration Monitoring sensor fusion sensors Cameras Intelligent sensors Temperature sensors Autonomous system crowd monitoring data fusion decision fusion sensor fusion

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2023 IEEE 11th Region 10 Humanitarian Technology conference, R10-HTC 2023

2023 IEEE 11th Region 10 Humanitarian Technology Conference,...

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11th IEEE Region 10 Humanitarian Technology conference, R10-HTC 2023

ISBN: (纸本)9798350326147

The proceedings contain 195 papers. The topics discussed include: obstacle collision warning using ultrasonic and lidar sensor fusion;web and android application for real estate business management;proposing novel recurrent neural network architectures for infant cry detection in domestic context;design and implementation of fractal antenna for wireless applications, analysis using machine learning;medicinal plant species identification using AI;smart power systems for smart cities: architectural development and economic performance assessment;XGB-RFE: an XGBoost approach for improved playback spoofing detection in automatic speaker verification systems using recursive feature elimination;and low-cost sensors based PM profiling using aerial platform.

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sensor fusion of 3D Time-of-Flight and Thermal Infrared Camera for Presence Detection of Living Beings

Sensor Fusion of 3D Time-of-Flight and Thermal Infrared Came...

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IEEE sensors conference

作者： Oppliger, Moritz Gutknecht, Jonas Gubler, Roman Ludwig, Matthias Loeliger, Teddy ZHAW Zurich Univ Appl Sci Inst Signal Proc & Wireless Commun Winterthur Switzerland

ISBN: (纸本)9781665484640

Presence detection of living beings is a key processing step in many applications. To increase the detection accuracy, an algorithm that uses camera fusion of a 3D Time-of-Flight (3D ToF) and a Long-Wave Infrared (LWIR) camera is applied and characterized here. Different sensor fusion approaches and sensor data processing schemes are compared. The algorithms are applied to a typical wildlife detection dataset and their performance is evaluated. The detection accuracy is up to 10 % higher when combining 3D ToF and LWIR images compared to only 3D ToF or LWIR images. Additionally, the location and size of the living being can be determined using the 3D ToF image.

关键词： 3D Time-of-Flight (3D ToF) Long-Wave Infrared (LWIR) Thermal Imaging Camera fusion sensor fusion

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R2L-SLAM: sensor fusion-Driven SLAM using mmWave Radar, LiDAR and Deep Neural Networks

R2L-SLAM: Sensor Fusion-Driven SLAM using mmWave Radar, LiDA...

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IEEE sensors conference

作者： Balemans, Niels Hooft, Lucas Reiter, Philippe Anwar, Ali Steckel, Jan Mercelis, Siegfried Univ Antwerp Fac Appl Engn Groenenborgerlaan 171 B-2020 Antwerp Belgium Univ Antwerp IMEC Fac Appl Engn IDLab Sint Pietersvliet 7 B-2000 Antwerp Belgium Univ Antwerp Fac Appl Engn Cosys Lab B-2000 Antwerp Belgium Flanders Make Strateg Res Ctr Lommel Belgium

ISBN: (纸本)9798350303872

Optical sensing modalities are extensively used in autonomous vehicles (AVs). These sensors are, however, not always reliable, particularly in harsh or difficult sensing conditions, such as with smoke or rain. This limitation can impact their application potential due to safety concerns, since optical sensors can fail to reliably perceive obstacles in such harsh conditions. To address this, it would be desirable to include other modalities, such as radar, into the perception sensor suites of these AVs. However, this is difficult because many recent state-of-the-art navigation algorithms are designed specifically for LiDAR sensors. In this work, we propose a modality prediction method that allows for the addition of a single-chip mmWave radar sensor to an existing sensor setup consisting of a 2D LiDAR sensor, without changing the current downstream applications. We demonstrate the increased reliability of our method in situations where optical sensing modalities become less accurate and unreliable.

关键词： sensor systems mmWave Radar Autonomous vehicles Navigation Deep learning

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Advancement in SLAM Techniques and Their Diverse applications 12

Advancement in SLAM Techniques and Their Diverse Application...

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12th International conference on System Modeling and Advancement in Research Trends, SMART 2023

作者： Dewan, Abhishek Kumar, Ashutosh Singh, Hariom Solanki, Vishist Singh Kaur, Parampreet Chandigarh University Mohali India

ISBN: (纸本)9798350369861

Simultaneous Localization and Mapping (SLAM) is a pivotal technology at the intersection of robotics, computer vision, and autonomous systems. This comprehensive review paper explores the dynamic landscape of SLAM techniques and their multifaceted applications. Beginning with an illuminating background on the fundamental principles of SLAM, we traverse the rich tapestry of advancements in SLAM methodologies. The paper categorizes SLAM techniques into distinct paradigms, including feature-based SLAM, pose-graph SLAM, visual SLAM, and sensor fusion, elucidating their underlying algorithms, strengths, and limitations. From the intricate interplay of sensors and data sources, SLAM has matured into a versatile toolset, facilitating a broad spectrum of applications. Our exploration extends to the diverse domains benefiting from SLAM, including robotics and autonomous navigation, augmented reality, autonomous vehicles, indoor mapping, agriculture, healthcare, and more. Each application domain is examined, emphasizing the specific challenges encountered and the innovative solutions that have arisen. © 2023 IEEE.

关键词： Computer vision

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A New Paradigm of Computer Art Design based on fusion of Light Collection sensors and Smart Image Detection algorithms

A New Paradigm of Computer Art Design based on Fusion of Lig...

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2022 International conference on Edge Computing and applications, ICECAA 2022

作者： Wang, Chengwen College of Fine Arts Northwest Normal University Gansu Lanzhou730070 China

ISBN: (数字)9781665482325

ISBN: (纸本)9781665482325

The use of image recognition technology can complete tasks that cannot be achieved by traditional sensor technology, and at the same time, with the help of image recognition and multi-source information fusion, the monitoring effect can be better and more accurate. With the continuous development of computer technology. Based on the principle of sparse representation, integrating cutting-edge computer technology into art design can simplify the difficulty of art design work. This article takes Photoshop technology as an example to illustrate the teaching research of Photoshop in art design work. Select the M nearest neighbors of the test sample to replace the original training sample;weighted decision fusion. The experimental results on the CSIST face database show that, compared with similar algorithms, the proposed algorithm improves the recognition rate and calculation speed. © 2022 IEEE.

关键词： Image recognition

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Mitigation of Motion Artifacts in Pulse Oximetry through Redundant sensors 25

Mitigation of Motion Artifacts in Pulse Oximetry through Red...

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25th IEEE conference on Signal Processing - algorithms, architectures, Arrangements, and applications (IEEE SPA)

作者： Alkhoury, Ludvik Choi, Jiwon Wang, Chizhong Mahoney, Sean Shender, Barry S. Hrebien, Leonid Kam, Moshe New Jersey Inst Technol Dept Elect & Comp Engn Newark Coll Engn Newark NJ 07102 USA Athena GTX Regulatory Affairs Dept Athena IA 50113 USA Naval Air Warfare Ctr Aircraft Div Human Syst Dept Patuxent River MD 20670 USA Drexel Univ Dept Elect & Comp Engn Philadelphia PA 19104 USA

ISBN: (数字)9788362065424

ISBN: (纸本)9788362065424

Photoplethysmography (PPG) signals are used in pulse oximetry to calculate peripheral capillary oxygen saturation (SpO(2)) levels. It is well known that the accuracy of the calculated SpO(2) is susceptible to motion noise. Using field data collected from human subjects during rest and while exhibiting medium to high-intensity physical exercises, we show how sensor redundancy can mitigate motion effects by employing two channels of red and infrared PPG signals. We demonstrate the advantage of redundancy using two SpO(2) calculation methods. These methods are widely used "Red over Infrared" (RoI) method and the Discrete Saturation Transform (DST) algorithm both, assisted by pre-filtering by a heart-rate tuned comb filter. The data were collected from individuals at rest and while exercising. SpO(2) levels are calculated for two channels, each using a red and an infrared PPG signal. We integrated the SpO(2) computed from the two channels with a Kalman filter (KF) and computed the SpO(2) mean absolute error (MAE) from each channel individually and after integrating the two channels. The SpO(2) MAEs of each one of the two channels separately were greater (and statistically significant) than the SpO(2) MAE obtained from the two channels integrated with a KF. We observed that when two channels of PPG (red and infrared) signals were available, then combining SpO(2) levels obtained from these two channels exhibited a smoother and more reliable estimate of the blood oxygen saturation level as compared the SpO(2) levels computed from each channel separately. The reason is that if the data for the two channels are collected at sufficient physical distance from one another, the motion artifacts affecting the two channels are uncorrelated. The KF is then able to use past measurements and modeling of the dynamics to attenuate the effect of the motion artifacts.

关键词： pulse oximetry peripheral capillary oxygen saturation (SpO(2)) electrocardiography (ECG) motion artifact motion resistant oximetry data fusion comb filter red over infrared Discrete Saturation Transform (DST) Kalman Filter wearable unit multiple photoplethysmography (PPG) channels

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Parallel Computing architectures for Robotic applications: A Comprehensive Review

arXiv

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arXiv 2024年

作者： Islam, Md Rafid Electrical and Computer Engineering North South University Dhaka Bangladesh

With the growing complexity and capability of contemporary robotic systems, the necessity of sophisticated computing solutions to efficiently handle tasks such as real-time processing, sensor integration, decision-making, and control algorithms is also increasing. Conventional serial computing frequently fails to meet these requirements, underscoring the necessity for high-performance computing alternatives. Parallel computing, the utilization of several processing elements simultaneously to solve computational problems, offers a possible answer. Various parallel computing designs, such as multi-core CPUs, GPUs, FPGAs, and distributed systems, provide substantial enhancements in processing capacity and efficiency. By utilizing these architectures, robotic systems can attain improved performance in functionalities such as real-time image processing, sensor fusion, and path planning. The transformative potential of parallel computing architectures in advancing robotic technology has been underscored, real-life case studies of these architectures in the robotics field have been discussed, and comparisons are presented. Challenges pertaining to these architectures have been explored, and possible solutions have been mentioned for further research and enhancement of the robotic applications. © 2024, CC BY.

关键词： Decision making

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Distance Estimation by fusion of Monocular Ranging and MMW Radar Information

Distance Estimation by Fusion of Monocular Ranging and MMW R...

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International conference on Vision, Image and Signal Processing (ICVISP)

作者： QiRui Cui Ye Chen ShunGeng Wang School of engineer Huzhou University Huzhou Zhejiang China

ISBN: (数字)9798331541460

ISBN: (纸本)9798331541477

A single sensor often fails to provide comprehensive and reliable information in complex environments. However, multi-sensor fusion leverages the complementary strengths of different sensors, fully utilizing their measurement capabilities and effectively mitigating the limitations of individual sensors. In practical applications, sensor data is frequently influenced by environmental noise, resulting in increased measurement errors. To overcome this challenge, this paper presents a monocular distance measurement model based on object detection algorithms, which is combined with millimeter-wave radar for sensor fusion, aiming to enhance measurement accuracy and system robustness. The proposed method first constructs a geometric model for monocular distance measurement based on object detection algorithms. Subsequently, a dynamic error model is introduced to address the issue of sensor errors varying with distance. Finally, Kalman filtering is applied to fuse data from multiple sensors. Simulation results demonstrate that the proposed approach effectively reduces sensor measurement noise, significantly improves distance measurement accuracy, and meets the requirements of practical applications.

关键词： Accuracy Fuses Heuristic algorithms Geometric modeling Signal processing algorithms Radar detection Object detection Millimeter wave radar Distance measurement Kalman filters

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Infrared and Visible Image fusion based on Deep Gradient Constraint Driven Network 23

Infrared and Visible Image Fusion based on Deep Gradient Con...

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23rd IEEE International conference on High Performance Computing and Communications, 7th IEEE International conference on Data Science and Systems, 19th IEEE International conference on Smart City and 7th IEEE International conference on Dependability in sensor, Cloud and Big Data Systems and applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Wang, Ze Xu, Guoxia Center of China Academy of Launch Vehicle Technology Beijing100076 China Nanjing University of Posts and Telecommunications Jiangsu Nanjing210000 China

ISBN: (纸本)9781665494571

Infrared and visible image fusion technology refers to the fusion of thermal radiation information and texture details contained in infrared image and visible image respectively, so as to obtain high-quality fused images, which has important applications in intelligent monitoring, target detection and tracking, etc. The two images are based on different imaging principles. How to fuse the advantages of their respective images and ensure that the images are not lost is the key of fusion technology. Different from the traditional algorithms that only focus on the global information of superimposed images, this paper proposes a fusion algorithm that pays more attention to the local gradient information of images to extract more detailed gradient information. Experiments show that our algorithm is more effective and advanced. © 2021 IEEE.

关键词： Target tracking Smart cities Fuses Imaging Object detection Data mining Convolutional neural networks

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