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检索条件"任意字段=Conference on Real-Time Image and Video Processing 2010"

共 8710 条记录，以下是271-280 订阅

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Enhancing Performance and Reducing Injury Risk: Analyzing Basketball Players' Jumping Landing Form through image processing and Support Vector Machine Algorithm 4

Enhancing Performance and Reducing Injury Risk: Analyzing Ba...

引用

4th IEEE International conference on Electronic Communications, Internet of Things and Big Data, ICEIB 2024

作者： Bumagat, Royce D. Uy, Sebastian Paul O. Balbin, Jessie Jaye R. School of Electronics Electrical and Computer Engineering Mapua University Manila Philippines Manila Philippines

ISBN: (纸本)9798350360721

Basketball involves frequent direction changes, quick acceleration, and big jumps, which increase the risk of landing-related injuries. Ankle sprains and knee injuries, such as ACL tears, are common among basketball players. Thus, we developed an analytical system to assess a basketball player's jump-landing performance with reduced risks of injuries. The system adopted an image processing algorithm, specifically Support Vector Machine and image processing techniques, to evaluate jumping landing techniques. The system was implemented using HTML, CSS, PHP, and JavaScript, providing real-time video feedback and evaluation reports. The accuracy of the system was validated through the expertise of a basketball coach. Data was collected using a Kinect sensor and a laptop webcam, and specialized software processed the data to extract relevant features. The system's performance was compared with assessments by coaches, and the collected data and analysis results were stored. The developed system accurately identified ideal jump landing techniques by assessing joint angles and key components of the landing. The analysis result showed strong correlations between evaluations and reference postures. The system can be used to reduce the risk of injuries and improve player performance in basketball. The study's findings provide promising implications for player safety and performance enhancement. © 2024 IEEE.

关键词： Support vector machines

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Optimization Techniques of Deep Learning Models for Visual Quality Improvement 22

Optimization Techniques of Deep Learning Models for Visual Q...

引用

22nd International conference on New Trends in Intelligent Software Methodologies, Tools and Techniques, SoMeT 2023

作者： Palloni, Lorenzo Galteri, Leonardo Bertini, Marco University of Florence Italy Pegaso Telematic University Italy

ISBN: (纸本)9781643684307

video restoration is a widely studied task in the field of computer vision and image processing. The primary objective of video restoration is to improve the visual quality of degraded videos caused by various factors, such as noise, blur, compression artifacts, and other distortions. In this study, the integration of post-Training quantization techniques was investigated to optimize deep learning models for super-resolution inference. The results indicate that reducing the precision of weights and activations in these models substantially decreases the computational complexity and memory requirements without compromising performance, rendering them more practical and cost-effective for real-world applications, where real-time inference is often required. When TensorRT was integrated with PyTorch, the efficiency of the model was further improved taking advantage of the INT8 computational capabilities of recent NVIDIA GPUs. © 2023 IOS Press. All rights reserved.

关键词： image reconstruction

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Research on Intelligent Automatic Assembly Pipeline Detection System Based on FPGA video image Technology 5

Research on Intelligent Automatic Assembly Pipeline Detectio...

引用

5th IEEE International conference on Power, Intelligent Computing and Systems, ICPICS 2023

作者： Juan, Gao Mingjin, Li Juncheng, Ma Shuang, Xie Xinrui, Peng School of Chengdu College of University of Electronic Science and Technology of China Sichuan Chengdu China

ISBN: (纸本)9798350333442

This paper proposes the design of video image hardware structure processing based on FPGA. This improves the processing speed and real-time performance of the intelligent automatic assembly pipeline inspection system. The system uses background difference method to detect moving targets. The static scene in the image is eliminated by binarization method. Morphological filtering is then done to eliminate voids and noise points. Finally, the target center point is extracted. Adopt modular design and pipeline processing mode. The high-speed parallel processing of FPGA and large capacity of DDR2SDRAM are fully utilized. A standard equipment for epitope error consistency and a fault simulator are designed. The control software for the test system is developed. The analysis model of calibration data is established. The measurement uncertainty analysis process of test results is given based on GUM algorithm. Practice test results show that the system can effectively detect moving objects. © 2023 IEEE.

关键词： Field programmable gate arrays (FPGA)

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AI based Analysis of Hyperspectral images with Spectacular Efficacy in video Analytics

AI based Analysis of Hyperspectral Images with Spectacular E...

引用

2024 International conference on Intelligent and Innovative Practices in Engineering and Management, IIPEM 2024

作者： Jadhav, Samarth Yogesh More, Rutuja Rajatam Tingare, Bhagyashree Ashok Lakshmi, G. Prasanna Kolse, Shivam Darwante, N.K. Sanjivani College of Engineering Department of Information Technology MH Kopargaon India D Y Patil College of Engineering Akurdi Department of Artificial Intelligence and Data Science Pune India Sandip University School of Computer Science and Engineering Maharashtra Nashik India Sanjivani College of Engineering Department of Electronics and Computer Engineering MH Kopargaon India

ISBN: (纸本)9798350390049

Hyperspectral imaging and artificial intelligence (AI) have transformed imaging and data processing through their ability to capture and analyze detailed spectral information. This paper explores the integration of hyperspectral imaging with AI, focusing on its impact on video analytics. Hyperspectral imaging provides comprehensive, multi-dimensional data by capturing a wide range of spectral wavelengths, enabling precise material identification and environmental monitoring beyond traditional RGB imaging. The synergy between hyperspectral imaging and AI enhances real-time analysis and decision-making by leveraging deep learning algorithms for pattern recognition and anomaly detection. This study introduces a novel AI-based hyperspectral image analysis approach for video analytics, utilizing convolutional neural networks (CNNs) and hybrid CNN-attention models to improve object recognition and classification. The methodology is validated through experiments that measure classification accuracy, processing speed, and resilience to varying conditions. Results demonstrate significant improvements in accuracy and efficiency over traditional methods, highlighting the potential for advanced applications in surveillance, environmental monitoring, and industrial quality control. © 2024 IEEE.

关键词： Convolutional neural networks

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Design of real-time image processing system of medical high-definition electronic endoscope based on FPGA 28

Design of real-time image processing system of medical high-...

引用

28th International conference on Mechatronics and Machine Vision in Practice, M2VIP 2022

作者： Meng, Peng Zhang, Hui Wen, Haiying Min, Dai Zhang, Zhisheng Zhang, Guifu School of Mechanical Engineering Southeast University Seu Nanjing China

ISBN: (纸本)9798350334333

Medical high-definition electronic endoscopes have high requirements on real-time performance and video quality. Compared with software-based image algorithms, the algorithms based on field programmable gate array (FPGA) have higher real-time performance, but are more difficult to implement. Referring to the working requirements of the medical high-definition electronic endoscope image processing system, an experimental platform for the real-time image processing system based on FPGA is designed in this paper. And the FPGA implementation of lens shading correction, white balance and gamma correction is studied in this system. The hardware consumption is effectively reduced by improving the relevant algorithm equations. After these three kinds of processing, the output image quality is obviously improved, and high-definition output is realized. At the same time, the total time delay of three kinds of image signal processing based on FPGA is within 16 working clock cycles, which can well meet the real-time working requirements of endoscope. The design of this system has certain reference for the research of medical high-definition electronic real-time image processing system. The realization of the three algorithms also enriches the research of FPGA-based image processing algorithms. © 2022 IEEE.

关键词： Endoscopy

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Research on image Recognition Technology Based on Artificial Intelligence under Computer Big Data 2

Research on Image Recognition Technology Based on Artificial...

引用

2nd IEEE International conference on Data Science and Computer Application, ICDSCA 2022

作者： Linjun, Li Chengdu University of Technology College of Computer Science and Cyber Security Oxford Brookes College Chengdu China

ISBN: (数字)9781665472005

ISBN: (纸本)9781665472005

This paper develops a remote video monitoring system based on ARM;the hardware of the system is based on the ARM S3C2440 embedded chip, and the circuit structure of the main modules such as power supply, Ethernet interface, JTAG interface, and data storage of the hardware system is designed;The software of the system uses Linux as the operating system, uses video4 Linux to complete video image acquisition, uses JPEG image compression technology to realize video compression processing, and uses real-time transmission protocol RTP to realize video image encapsulation, network transmission, and control. The experimental results show that the method can simultaneously perform real-time online detection of various anomalies such as occlusion, blurring and scene switching that suddenly appear in remote video surveillance, and the accuracy rate can reach 88.75%. © 2022 IEEE.

关键词： image recognition

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PhysID: Physics-based Interactive Dynamics from a Single-view image

PhysID: Physics-based Interactive Dynamics from a Single-vie...

引用

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

作者： Gothe, Sourabh Vasant Chattopadhyay, Ayon Kiran, Gunturi Venkata Sai Phani Pratik Agarwal, Vibhav Vachhani, Jayesh Rajkumar Ghosh, Sourav Parameswaranath, V.M. Barath Raj, K.R. Samsung R&D Institute India - Bangalore India

ISBN: (纸本)9798350368741

Transforming static images into interactive experiences remains a challenging task in computer vision. Tackling this challenge holds the potential to elevate mobile user experiences, notably through interactive and AR/VR applications. Current approaches aim to achieve this either using pre-recorded video responses or requiring multi-view images as input. In this paper, we present PhysID, that streamlines the creation of physics-based interactive dynamics from a single-view image by leveraging large generative models for 3D mesh generation and physical property prediction. This significantly reduces the expertise required for engineering-intensive tasks like 3D modeling and intrinsic property calibration, enabling the process to be scaled with minimal manual intervention. We integrate an on-device physics-based engine for physically plausible real-time rendering with user interactions. PhysID represents a leap forward in mobile-based interactive dynamics, offering real-time, non-deterministic interactions and user-personalization with efficient on-device memory consumption. Experiments evaluate the zero-shot capabilities of various Multimodal Large Language Models (MLLMs) on diverse tasks and the performance of 3D reconstruction models. These results demonstrate the cohesive functioning of all modules within the end-to-end framework, contributing to its effectiveness. © 2025 IEEE.

关键词： 3D Modelling Diffusion Multimodal Large Language Models Physics-Based Rendering

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Deep learning architecture search for real-time image denoising

Deep learning architecture search for real-time image denois...

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conference on real-time image processing and Deep Learning

作者： Hernandez, Esau A. Hervert Cao, Yan Kehtarnavaz, Nasser Univ Texas Dallas Dept Math Sci Richardson TX 75080 USA Univ Texas Dallas Dept Elect & Comp Engn Richardson TX 75080 USA

ISBN: (纸本)9781510650817;9781510650800

Deep learning has been widely used in recent years to accomplish many tasks such as image classification, natural language processing, and image denoising amongst others. However, the process to create deep neural networks by trial and error can often be very repetitive and time consuming and it is not clear if the entire network architecture space is explored towards finding an optimum architecture. This paper presents a systematic and automatic way to design or find an optimal architecture of deep neural networks. First, a sensitivity analysis is carried out on the parameters of interest of a network in order to identify those parameters which are most influential to the performance of the network. A search space is defined based on these parameters. Reinforcement learning is then used to find an optimal architecture within this search space. In this paper, our developed method of finding an optimal network architecture is applied to the problem of image denoising. In particular, the emphasis is placed on the Densely Connected Hierarchical Network (DHDN). A resulting network, named ENAS-DHDN, is shown to marginally outperform the original network suggesting that the original network is close to optimal. After finding an optimal network, it is used to estimate the time to process Standard Definition (SD) and High Definition (HD) videos with a frame rate of 30fps indicating that real-time video denoising at the SD resolution is achievable.

关键词： image Denoising Optimal Architecture of Deep Neural Networks Reinforcement Learning real-time image Denoising

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real-time image and video processing Applications Using Raspberry Pi 1

Real-Time Image and Video Processing Applications Using Rasp...

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1st IEEE Industrial Electronics Society Annual On-Line conference, ONCON 2022

作者： Yildirim, Merve Karaduman, Ozgur Kurum, Hasan University of Firat Department of Electrical and Electronics Engineering Elazig Turkey University of Firat Department of Software Engineering Elazig Turkey

ISBN: (纸本)9798350398069

image and video processing applications have significant importance in many areas which are the industrial and medical applications, especially the vehicular technology. To provide safe driving, Driving Assistance Systems (DAS) including detection and tracking of a road situation, objects and behavior of the driver should be considered by using the image processing algorithms. Since these applications need the processors having high speed and power, a Raspberry Pi platform meeting these specifications is used in this study. A Canny edge detector which is one of the important edge detection methods, face and eye detection, the road line tracking, motion detection, and object detection applications are handled. Conversely the studies in the literature which are separately examined these techniques, all of them are realized as a real-time based on the Rapberry Pi according to a video image taken by a camera. For this reason, OpenCv and TensorFlow libraries having widely computer vision algorithms and Python software language are used. It is observed that the results of all image and video processing applications are successful and satisfying. As a result, a safe and comfortable driving can be provided thanks to the proposed methods. © 2022 IEEE.

关键词： Python

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Towards tiny object tracking for low-illumination wide-field

Towards tiny object tracking for low-illumination wide-field

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2022 International conference on Computer Application and Information Security, ICCAIS 2022

作者： Xie, Zhaodong Jia, Zhenhong Jiang, Wangxi College of Information Science and Engineering Xinjiang University Xinjiang Urumqi830046 China Key Laboratory of Signal Detection and Processing Xinjiang University Xinjiang Urumqi830046 China

ISBN: (纸本)9781510663459

Night is an inevitable scene for surveillance video. Due to the high image resolution, complex background, uneven illumination, and similarity between the target and the background of hawk-eye surveillance video, it is difficult for previous trackers to apply the tracking of a tiny object in such scenes. In this regard, this paper proposes to combine an online automatically and adaptively learning spatio-temporal regularized tracking algorithm with an efficient and effective low-light image enhancement algorithm to improve tracker performance. We constructed a new benchmark that includes 41 night surveillance sequences captured by Hawk-Eye cameras at night. Exhausted experiments have been conducted on this dataset, and the results show that by combining the two methods, the original algorithm can obtain better results in this dataset, and can meet the real-time object tracking, which contributes to the application of tiny object tracking in eagle-eye surveillance video at night. © 2023 SPIE.

关键词： image resolution

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