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检索条件"任意字段=Conference on Real-Time Image and Video Processing 2011"

共 8693 条记录，以下是201-210 订阅

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SDN- based Internet of video Things Platform Enabling real-time Edge/Cloud video Analytics 17

SDN- based Internet of Video Things Platform Enabling Real-T...

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IEEE 17th International conference on the Experience of Designing and Application of CAD Systems (CADSM)

作者： Kochan, Orest Beshley, Mykola Beshley, Halyna Shkoropad, Yuriy Ivanochko, Iryna Seliuchenko, Nadiia Hubei Univ Technol Sch Comp Sci Wuhan Peoples R China Lviv Polytech Natl Univ Dept Informat Measuring Technol 12 Bandery Str Lvov Ukraine Lviv Polytech Natl Univ Dept Telecommun 12 Bandery Str Lvov Ukraine Comenius Univ Fac Management Dept Informat Syst Bratislava 82005 Slovakia Lviv Polytech Natl Univ Dept Management & Int Business 12 Bandery Str Lvov Ukraine Lviv Polytech Natl Univ Dept Business Econ & Investment 12 Bandery Str Lvov Ukraine

ISBN: (纸本)9798350310856

The trend of recent years is the continuous development of the Internet of Things (IoT). Among such things, a significant share is occupied by visual sensors and video cameras that generate large amounts of data. In turn, the need to attract significant storage resources, transmission throughput, and processing power is an inevitable solution for real-time video analytics. Thus, the combination of smart cameras with the computing paradigm of Cloud/Edge and IoT architectures form the next generation of video surveillance systems, called the "Internet of video Things" (IoVT In this paper, a new IoVT platform is developed that, in addition to harmoniously combining Edge/Cloud computing, uses SDN to overcome challenges such as flexible management, control, and maintenance of IoVT devices. In particular, within the proposed IoVT platform, an algorithm for the dynamic selection of Edge or Cloud computing is implemented using an SDN controller to provide effective video analytics in real-time. This algorithm considers such parameters as the priority of computational tasks, the number of video streams, and the image quality with the ability to adapt to a specific application by software configuration of the IoVT platform. We also demonstrate the effectiveness of the proposed solutions on real equipment and discuss several promising areas of application of the developed platform.

关键词： Internet of video Things Software-defined Networking Cloud Computing Edge Computing Quality of Service

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Motion Recognition Model of Sports video Based on Feature Extraction Algorithm

Motion Recognition Model of Sports Video Based on Feature Ex...

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2023 IEEE International conference on image processing and Computer Applications, ICIPCA 2023

作者： Xiong, Feng Jiangxi Vocational Technical College of Industry & Trade Jiangxi Nanchang330038 China

ISBN: (纸本)9798350314670

With the rapid development of computer technology, network technology and multimedia technology, multimedia data is increasing exponentially. As an important part of video multimedia data, its structure is complex and its data volume is huge. Especially from a monocular camera, it is a very meaningful work to realize the correct recognition of human movements in any visual angle. video action recognition usually refers to the process of identifying human action categories from a video sequence. This technology is widely used in multimedia content analysis, human-computer interaction, intelligent real-time monitoring and other fields. It can be realized by feature extraction of video to generate feature vectors, and classification of feature vectors by classifier. To solve the above problems, this paper starts from the spatio-temporal correlation of video data, according to the mixed model structure of convolutional neural network (CNN) and long short term memory (LSTM), introduces the attention mechanism in semantic analysis, proposes a recursive machine network based on the attention mechanism, assigns different weights to each video frame, and improves the recognition effect in the captured video clips. © 2023 IEEE.

关键词： Feature extraction

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A real-time Background Replacement Method Based on Machine Learning for AR Applications 48

A Real-Time Background Replacement Method Based on Machine L...

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48th Annual IEEE International Computers, Software, and Applications conference (COMPSAC) - Digital Development for a Better Future

作者： Tsuboki, Yoshihiro Kawakami, Tomoya Matsumoto, Satoru Yoshihisa, Tomoki Teranishi, Yuuichi Univ Fukui Grad Sch Engn Fukui Japan Osaka Univ Cybermedia Ctr Osaka Japan Shiga Univ Fac Data Sci Hikone Shiga Japan Natl Inst Informat & Commun Technol Tokyo Japan

ISBN: (纸本)9798350376975;9798350376968

Recent technological advances in Virtual reality (VR) and Augmented reality (AR) enable users to experience a high-quality virtual world. Using VR to experience the virtual world, the user's entire view becomes the virtual world, and the user's physical movement is generally limited because the user cannot see the surrounding situation in the real world. Using AR to experience the virtual world, we generally use special sensors such as LiDAR to detect the real space and superimpose the virtual world on the real space. However, it is difficult for devices without such special sensors to detect real space and superimpose a virtual world at an appropriate position. This study proposes two methods for replacing the background: a method using depth estimation and a method using semantic segmentation. This study also confirmed that the system can be used with sufficient removal accuracy and response time by using appropriate image size for the environment and that a safe and highly immersive virtual world experience can be achieved.

关键词： augmented reality virtual reality video processing dynamic background replacement mobile computing

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Design and Development of real-time image Based Fire Detection Using OpenCV and HSV

Design and Development of Real-Time Image Based Fire Detecti...

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2024 International conference on Advances in Computing Research on Science Engineering and Technology, ACROSET 2024

作者： Bothraa, Raajjhesh Ahire, Darsh Bhadange, Rutuja Temkar, Rutvij Mane, Mayuri Jamdhade, Shweta University of Mumbai Departement of Information Technology Mumbai India

ISBN: (纸本)9798350388800

Nowadays fire detection and recognition are one of the major securities and security region for saving human life. Concern to this we are going to propose a new model for early detection and recognition of the fire for the purpose to provide the better services. In this proposed model we are going to use The OpenCV and HSV driven Fire Detection initiative is aimed at recognizing fires by utilizing image processing technology to give early alerts to people. This proposed system is launched to overcome limitations like enhance efficiency and instant alert to avoid disastrous situation through. The existing automated fire alarm systems like the sensor method have limitations in detecting smoke-caused fires in restricted areas and late alerts. By employing a central processing unit and connecting a webcam as hardware, video footage from the surroundings is captured in the form of image as an input source. The components of theory emphasize computer vision, deep learning, image processing, color models, and the algorithm operation for detecting fires. This endeavor is designed to offer a comprehensive understanding of object detection using computers and the flexibility of these technologies in various applications. The utilization of OpenCV serves the purpose of computer vision in this system. The complete code is written in Python language utilizing the open CV library. © 2024 IEEE.

关键词： Deep learning

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Rosette Plant Centre Detection and Tracking using YOLO: An Efficient Deep Learning Approach 3

Rosette Plant Centre Detection and Tracking using YOLO: An E...

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3rd International conference on Computing and Machine Intelligence (ICMI)

作者： Akagic, Amila Saric, Rijad Buza, Emir Kecman, Stefani Lewsey, Mathew G. Custovic, Edhem Whelan, James Univ Sarajevo UNSA Fac Elect Engn Sarajevo 71000 Bosnia & Herceg La Trobe Inst Sustainable Agr & Food LISAF Dept Anim Plant & Soil Sci Melbourne Vic 3086 Australia La Trobe Univ Australian Res Council Res Hub Med Agr Melbourne Vic 3086 Australia Sci Instruments Australia SIA 2 Res Ave Melbourne Vic 3086 Australia Zhejiang Univ Coll Life Sci State Key Lab Plant Environm Resilience Hangzhou 310058 Peoples R China Zhejiang Univ Prov Int Sci & Technol Cooperat Base Engn Biol Haining 314400 Peoples R China

ISBN: (纸本)9798350372977;9798350372984

The precise detection of plant centres is important for growth monitoring, enabling the continuous tracking of plant development to discern the influence of diverse factors. It holds significance for automated systems like robotic harvesting, facilitating machines in locating and engaging with plants. In this paper, we explore the YOLOv4 (You Only Look Once) real-time neural network detector for plant centre detection. Our dataset, comprising over 12,000 images from 151 Arabidopsis thaliana accessions, is used to fine-tune the model. Evaluation of the dataset reveals the model's proficiency in centre detection across various accessions, boasting an mAP of 99.79% at a 50% IoU threshold. The model demonstrates real-time processing capabilities, achieving a frame rate of approximately 50 FPS. This outcome underscores its rapid and efficient analysis of video or image data, showcasing practical utility in time-sensitive applications.

关键词： Plant Phenotyping Arabidopsis thaliana Computer Vision image processing Deep Learning Neural Networks

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image processing Based Speed Measurement and License Plate Detection of Vehicles 2

Image Processing Based Speed Measurement and License Plate D...

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2nd IEEE International conference on Advances in Computing, Communication and Applied Informatics, ACCAI 2023

作者： Sherin, K. Praneet Kumar, B. Kumar R, Prasath St. Joseph's Institute of Technology Tamil Nadu Chennai600119 India

ISBN: (纸本)9798350315905

The majority of fatalities, injuries, and property damage are caused by traffic collisions. The principal cause of death is frequent excessive speeding. Deep learning techniques are now frequently used for the analysis of traffic surveillance footage thanks to the recent significant improvements in the computation power of common computers. The system for detecting vehicle speed was created utilizing video processing methods. Through the application of image and video processing techniques, moving vehicle velocity is estimated utilizing vehicle speed detection. real-time speed analysis of video is performed without the use of camera calibration. Moving vehicles are segregated by using masking and frame subtraction methods. In this technique a video is recorded using a fixed-focus camera while taking into account a number of factors. image Enhancement is used to enhance the qualities of each frame of the video captured using the camera. The vehicle has a bounding box applied to it to identify it, and each frame's pixel change indicates the car's motion. The speed is then calculated using a distance formula while taking Pixel Per Meter into account. Pixel Per Meter is a unit of measurement that is used to describe how much potential image detail a camera gives at a specific distance. The proposed model's outcomes are differed with those of other models. The proposed method is efficient and can determine whether a result is correct up to 96.3% of the time. Results obtained demonstrate the system's effectiveness. © 2023 IEEE.

关键词： Object detection

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Towards real-time video Compressive Sensing on Mobile Devices 24

Towards Real-time Video Compressive Sensing on Mobile Device...

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32nd ACM International conference on Multimedia, MM 2024

作者： Cao, Miao Wang, Lishun Wang, Huan Wang, Guoqing Yuan, Xin Zhejiang University Zhejiang Hangzhou China Westlake University Zhejiang Hangzhou China University of Electronic Science and Technology of China Sichuan Chengdu China

ISBN: (纸本)9798400706868

video Snapshot Compressive Imaging (SCI) uses a low-speed 2D camera to capture high-speed scenes as snapshot compressed measurements, followed by a reconstruction algorithm to retrieve the high-speed video frames. The fast evolving mobile devices and existing high-performance video SCI reconstruction algorithms motivate us to develop mobile reconstruction methods for real-world applications. Yet, it is still challenging to deploy previous reconstruction algorithms on mobile devices due to the complex inference process, let alone real-time mobile reconstruction. To the best of our knowledge, there is no video SCI reconstruction model designed to run on the mobile devices. Towards this end, in this paper, we present an effective approach for video SCI reconstruction, dubbed MobileSCI, which can run at real-time speed on the mobile devices for the first time. Specifically, we first build a U-shaped 2D convolution-based architecture, which is much more efficient and mobile-friendly than previous state-of-the-art reconstruction methods. Besides, an efficient feature mixing block, based on the channel splitting and shuffling mechanisms, is introduced as a novel bottleneck block of our proposed MobileSCI to alleviate the computational burden. Finally, a customized knowledge distillation strategy is utilized to further improve the reconstruction quality. Extensive results on both simulated and real data show that our proposed MobileSCI can achieve superior reconstruction quality with high efficiency on the mobile devices. Particularly, we can reconstruct a 256x256x8 snapshot compressed measurement with real-time performance (about 35 FPS) on an iPhone 15. Code is available at https://***/mcao92/MobileSCI. © 2024 Owner/Author.

关键词： image compression

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real-time image Edge Detection System Design and Algorithms for Artificial Intelligence FPGAs 2

Real-time Image Edge Detection System Design and Algorithms ...

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2nd IEEE International conference on Artificial Intelligence of Things and Crowdsensing, AIoTCs 2022

作者： Ye, Caizeng Shandong Institute of Commerce and Technology Jinan250103 China

ISBN: (纸本)9798350334104

Extraction of image edges is the basis of image processing class algorithms, for image edge extraction is of great significance. Nowadays, video image data is greatly increases the amount of image data and the processing difficulty. FPGA technology stands out for its parallel processing method and pipeline structure, which enables high-speed processing of large amounts of image data, and its high processing speed ensures real-time image data processing. The application of FPGA technology in the field of image data processing will be unstoppable. The main objective of this paper is to investigate the design and algorithms of a real-time image edge detection system using artificial intelligence and FPGAs. Median filtering and edge detection algorithms in video acquisition and pre-processing systems are an important part of complex algorithms in the field of target recognition and tracking, and the quality of image pre-processing plays an important role in the high accuracy of subsequent algorithms. This paper presents an in-depth study and analysis of the hardware implementation of the HD video capture and pre-processing system, combining the characteristics of FPGA parallel high-speed and pipeline structure, and completes the system design and verification on the hardware platform, fully proving the feasibility and correctness of the system. © 2022 IEEE.

关键词： Field programmable gate arrays (FPGA)

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On Pre-chewing Compression Degradation for Learned video Compression

On Pre-chewing Compression Degradation for Learned Video Com...

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IEEE International conference on Visual Communications and image processing (VCIP)

作者： Ho, Man Minh Sun, Heming Zhang, Zhiqiang Zhou, Jinjia Hosei Univ Tokyo Japan Waseda Univ Tokyo Japan JST PRESTO Tokyo Japan

ISBN: (纸本)9781665475921

Artificial Intelligence (AI) needs huge amounts of data, and so does Learned Restoration for video Compression. There are two main problems regarding training data. 1) Preparing training compression degradation using a video codec (e.g., Versatile video Coding - VVC) costs a considerable resource. Significantly, the more Quantization Parameters (QPs) we compress with, the more coding time and storage are required. 2) The common way of training a newly initialized Restoration Network on pure compression degradation at the beginning is not effective. To solve these problems, we propose a Degradation Network to pre-chew (generalize and learn to synthesize) the real compression degradation, then present a hybrid training scheme that allows a Restoration Network to be trained on unlimited videos without compression. Concretely, we propose a QP-wise Degradation Network to learn how to compress video frames like VVC in real-time and can transform the degradation output between QPs linearly. The real compression degradation is thus pre-chewed as our Degradation Network can synthesize the more generalized degradation for a newly initialized Restoration Network to learn easier. To diversify training video content without compression and avoid overfitting, we design a Training Framework for Semi-Compression Degradation (TF-SCD) to train our model on many fake compressed videos together with real compressed videos. As a result, the Restoration Network can quickly jump to the near-best optimum at the beginning of training, proving our promising scheme of using pre-chewed data for the very first steps of training. In other words, a newly initialized Learned video Compression can be warmed up efficiently but effectively with our pre-trained Degradation Network. Besides, our proposed TF-SCD can further enhance the restoration performance in a specific range of QPs and provide a better generalization about QPs compared with the common way of training a restoration model. Our work is

关键词： Deep Learning video Compression Compression Degradation Semi-Supervised Learning

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Research on embedded system of image acquisition and real-time processing based on ZYNQ

Research on embedded system of image acquisition and real-ti...

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2022 International conference on Neural Networks, Information, and Communication Engineering, NNICE 2022

作者： Zhang, ZongJie Peng, Liu School of Electronic Information Engineering Changchun University of Science and Technology China

ISBN: (纸本)9781510655171

In order to meet the demand for flexible acquisition of video images and real-time processing of images, the speed and efficiency of image processing are improved. This paper is an embedded system based on the ZYNQ-7000 series chip which integrates ARM+FPGA structure for image acquisition and real-time processing. An improved sharpening algorithm that can be used for edge detection in the image processing module is proposed, and the algorithm is integrated into the hardware through the HLS tool that comes with Vivado, which realizes the flexible acquisition and real-time processing of video image data. By studying image processing algorithms commonly used in embedded acquisition and processing systems, an improved sharpening algorithm is designed based on threshold segmentation and image filtering and applied in experiments. In terms of hardware, it has the advantages of low power consumption, high performance and flexibility. In terms of algorithm, it not only improves the speed of image optimization processing, but also has a large development space and application value. © 2022 SPIE.

关键词： Embedded systems

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