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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201-210 订阅

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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...

引用

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...

引用

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...

引用

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...

引用

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...

引用

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

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

引用

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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Research on image Art Creation System of Integrated 3D Computer image Vision Digital Media 3

Research on Image Art Creation System of Integrated 3D Compu...

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3rd IEEE International conference on Electrical Engineering, Big Data and Algorithms, EEBDA 2024

作者： Liu, Yang Guo, Dongqi Institute of Arts and Media Guangzhou Vocational University of Science and Technology Guangzhou China Jeonbuk National University Department of Industrial Design Jeonju Korea Republic of

ISBN: (纸本)9798350380989

The accuracy and real-time performance of existing image labeling algorithms are not high. This project intends to study the video sequence feature labeling algorithm for digital multimedia images. A digital multimedia image segmentation method based on wavelet transform is proposed. Then the system adopts the color video segmentation method based on edge detection. Based on visual salience features, the model combines color features and gradient features to describe local image quality. The method fuses the brightness channels of the reference image and the corresponding distorted image, and calculates the similarity between the gradient map of the channel and the gradient map of the reference/distorted image. Target detection is realized by matching the target image with the gray information. Then the color feature and gradient feature are combined organically by means of the mean offset weight method, and a better image quality evaluation effect is obtained. The performance of many evaluation algorithms and has some advantages compared with the existing evaluation methods. © 2024 IEEE.

关键词： 3D image recognition media image digital processing visual feature

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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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real-time image-Based Automotive Sensing: A Practice on Fine-Grained Garbage Disposal 13

Real-Time Image-Based Automotive Sensing: A Practice on Fine...

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13th International conference on the Internet of Things, IoT 2023

作者： Huang, Wenhao Mikami, Kazuhiro Chen, Yin Nakazawa, Jin Keio University Japan Reitaku University Japan

ISBN: (纸本)9798400708541

This research presents a real-time automotive sensing system for the data of urban garbage disposal. The proposed solution is implemented on an edge computing device mounted on garbage truck where a deep learning based image processing algorithm is implemented to automatically counted the number of collected garbage bags from garbage collection video. A MQTT-based data server was developed to enable data publication from sensing device to server and data accumulation and to facilitate application development. Our system has the functions of high concurrency and low transmission delay, offline reconnection, breakpoint transmission and client authentication. This work is to provide a real-time, low-cost, reliable and replicable system for the implementation of a widespread sensing network for automotive edge computing and smart city applications. © 2023 Owner/Author.

关键词： Smart city

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