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

共 8711 条记录，以下是311-320 订阅

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The Abnormal Events Detection System by Feature Enhancement-Based Deep Learning Network 12

The Abnormal Events Detection System by Feature Enhancement-...

引用

12th IEEE Global conference on Consumer Electronics, GCCE 2023

作者： Chen, Chao-Ho Chen, Tsong-Yi Chen, Yong-Lin Liu, Bing-Hong Jou, Jau-Ji Wen, Cheng-Kang National Kaohsiung University of Science and Technology Department of Electronic Engineering Taiwan Tainan University of Technology Department of Information Management Taiwan

ISBN: (纸本)9798350340181

To achieve real-time abnormal events detection that can assist the security personnel to manage the video surveillance system more efficient, this paper presents a feature enhancement-based deep learning network. The main strategy is to improve 2D convolutional architecture and employ the channel attention layer to enhance the important features between the layers, and combine DenseNet to strengthen the correlation between the features. Besides, a threshold-scoring strategy is designed to overcome the bad frame problem for substantially improving the accuracy of detecting abnormal events. Thus, the proposed system can effectively detect abnormal events with a higher accuracy rate than other approaches. Experimental results show that the proposed system can effectively detect abnormal events with a higher accuracy rate for various datasets, compared to other approaches, and can also achieve 33 fps (for real-time video processing). © 2023 IEEE.

关键词： image enhancement

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Research on Worksite's video image Safety Helmet Detection Method Based on Cloud-edge Cooperation 3

Research on Worksite's Video Image Safety Helmet Detection M...

引用

3rd International conference on Big Data, Artificial Intelligence and Internet of Things Engineering, ICBAIE 2022

作者： Lv, Jin Hao, Xiaolong Feng, Min Jia, Zheng Nanjing Nari Information and Communication Technology Co. Ltd. Nanjing China

ISBN: (数字)9781665451604

ISBN: (纸本)9781665451604

In order to ensure the safety of worksites staff, solve the problem that worksite monitoring video transmission occupies many network resources, has high time delay and cannot detect staff not wearing safety helmets in real time, a method for detecting safety helmets in video images of worksites based on cloud-edge cooperation is designed. The YOLOv3 algorithm is used for training the helmet detection model. Using the feature that the cloud-edge cooperation model can reduce processing latency, tasks such as model training and optimization, which are computationally intensive and have low real-time requirements, are handled by the cloud, and tasks such as target detection, which are relatively less computationally intensive and have high real-time requirements, are handled by the edge. Deploy the helmet detection model on the edge node to process the surveillance video images locally, and alarm in time when the behavior of not wearing a helmet is detected;the cloud server receives the surveillance video data and the processing results of the edge, and sends the information to the edge. The information and data are analyzed and calculated, the model is updated and optimized, and then the optimized model is pushed to the edge nodes, so as to realize the data interaction and automatic model optimization of the whole system. The experimental results show that this system can significantly reduce the surveillance video processing delay while maintaining a high recognition rate. © 2022 IEEE.

关键词： Edge computing

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Moving Object Detection and Speed Estimation by Digital image processing 1

Moving Object Detection and Speed Estimation by Digital Imag...

引用

1st International conference on Electronic Circuits and Signalling Technologies, ICECST 2022

作者： Khasim, K.N.V. Anilkumar, G. Vamshi, G. Ch, Sai Kumar Nikhil Yadav, M. ECE Gokaraju Rangaraju Institute of Engineering and Technology Bachupally Hyderabad India

Intelligent network systems and security surveillance are used for smart city development. Manual and heritage technology is used in surveillance systems to detect speeds higher than usual in developing countries. This process can be automated with ease by leveraging the potential of image processing. Moving object detection is one of the essential tasks in image processing because of its prominent role in many real-world applications. Vehicle speed detection can be achieved by employing image and video processing methods. In this work, a unique approach is proposed for moving object detection and speed estimation based on an integrated approach in video sequence frames as opposed to the most commonly used RADAR and LIDAR devices for traffic law enforcement. video data is collected and analyzed for speed in real-time without any sensor calibrations,thereby it removes any external hardware dependency requirements. Moving vehicles are segmented out by using frame-subtraction and masking techniques. The proposed algorithm tracks the time taken by the car to cover a predetermined fixed distance in order to calculate its speed. © Published under licence by IOP Publishing Ltd.

关键词： Python

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4th International conference on image processing and Capsule Networks, ICIPCN 2023

4th International Conference on Image Processing and Capsule...

引用

4th International conference on image processing and Capsule Networks, ICIPCN 2023

ISBN: (纸本)9789819970926

The proceedings contain 48 papers. The special focus in this conference is on image processing and Capsule Networks. The topics include: Multispectral Fusion of Multisensor image Data Using PCNN for Performance Evaluation in Sensor Networks;enhanced Feature Fusion from Dual Attention Paths Using Feature Gating Mechanism for Scene Categorization of Aerial images;Histopathology Breast Cancer Classification Using CNN;Brain Tumor Recognition from MRI Using Deep Learning with Data Balancing Methods and Its Explainability with AI;Multi-class Plant Leaf Disease Classification on real-time images Using YOLO V7;semantic image Segmentation of Agricultural Field Problem Areas Using Deep Neural Networks Based on the DeepLabV3 Model;u-Net-Based Segmentation of Coronary Arteries in Invasive Coronary Angiography;modern Challenges and Limitations in Medical Science Using Capsule Networks: A Comprehensive Review;securing Data in the Cloud: The Application of Fuzzy Identity Biometric Encryption for Enhanced Privacy and Authentication;Modified U-Net and CRF for image Segmentation of Crop images;flameGuard: A Smart System for Forest Fire Detection and Control;classification and Analysis of Chilli Plant Disease Detection Using Convolution Neural Networks;a New Multi-level Hazy image and video Dataset for Benchmark of Dehazing Methods;change Detection for Multispectral Remote Sensing images Using Deep Learning;studies on Movie Soundtracks Over the Last Five Years;an Enhanced real-time System for Wrong-Way and Over Speed Violation Detection Using Deep Learning;tea Leaf Disease Classification Using an Encoder-Decoder Convolutional Neural Network with Skip Connections;EEG Signal Feature Extraction Using Principal Component Analysis and Power Spectral Entropy for Multiclass Emotion Prediction;preface.

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Optimization and Implementation of image Defogging Algorithm Based on Field Programmable Gate Array 3

Optimization and Implementation of Image Defogging Algorithm...

引用

3rd International conference on Computer Science and Blockchain, CCSB 2023

作者： Hao, Zhenzhong Yu, Yao Sun, Jing Anhui Xinhua University School of Electronic Engineering School of Intelligent Manufacturing Hefei China Wuxi University School of Electronics and Information Engineering Wuxi China

ISBN: (纸本)9798350359077

images collected under severe weather conditions have problems such as poor contrast and reduced clarity. The deterioration of image quality limits the accuracy of computer vision and the efficiency of automated tasks. This article proposes an image dehazing algorithm based on contrast limited adaptive histogram equalization (CLAHE) and improved multi-scale retinex (MSR). In this algorithm, the input foggy degraded image is first processed by the contrast limited adaptive histogram equalization (CLAHE) algorithm and then the MSR algorithm. When processing the image with the MSR algorithm, the Gamma correction factor is introduced to estimate the incident light, and the surround function in the algorithm is optimized. The results show that compared with the original image, the image processed by this algorithm has improved the information entropy, average gradient, and standard deviation of the image. The hardware circuit was designed and the video real-time dehazing was successfully demonstrated on the field programmable gate array (FPGA), which improved the quality of the video image. A digital analysis of board-level resources and function consumption was conducted, proving that the hardware system in this article belongs to the low-power category. © 2023 IEEE.

关键词： image quality

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Anomaly Detection of Substation Engineering video Object Based on Cloud Platform

Anomaly Detection of Substation Engineering Video Object Bas...

引用

2023 IEEE International conference on Integrated Circuits and Communication Systems, ICICACS 2023

作者： Pu, Jian Zuo, Ting Information Center of Human Resources and Social Security Department of Yunnan Province Kunming China School of Construction Engineering Yunnan Land and Resources Vocational College Kunming China

ISBN: (纸本)9798350398465

video surveillance is a kind of computer platform, using digital image processing technology, through the acquisition, analysis and management of screen information to achieve various operations. video surveillance system plays a very important role in the power industry. In order to stabilize the substation system, it is necessary to monitor the substation project in real time. Judge whether the substation operates normally by video object anomaly detection. In this paper, the method of experimental detection and subtraction principle are used to detect the abnormalities of substation projects. The results show that the average relative error of the tower is about 2%, which verifies the accuracy and effectiveness of this method. © 2023 IEEE.

关键词： Electric substations

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Smart real-time multi-camera people locating system 25

Smart real-time multi-camera people locating system

引用

25th IEEE conference on Signal processing - Algorithms, Architectures, Arrangements, and Applications (IEEE SPA)

作者： Konieczka, Adam Balcerek, Julian Andrzejewski, Marcin Kaczmarek, Szymon Szczygiel, Jakub Poznan Univ Tech Div Signal Proc & Elect Syst Inst Automat & Robot Poznan Poland

ISBN: (数字)9788362065424

ISBN: (纸本)9788362065424

The article presents a multi-camera video monitoring system for rooms, which allows for real-time tracking of the places where individual people are. The system uses easily accessible, cheap cameras (e.g. installed in smartphones). video stream is sent via IP protocol to a central server. The server performs image analysis using the YOLO neural network The places where the detected people are located are marked on the virtual room plan that can be accessed using web browser.

关键词： people tracking people detection YOLO network image recognition

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real-time, Accurate, and Consistent video Semantic Segmentation via Unsupervised Adaptation and Cross-Unit Deployment on Mobile Device

Real-Time, Accurate, and Consistent Video Semantic Segmentat...

引用

IEEE/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： Park, Hyojin Yessenbayev, Alan Singhal, Tushar Adhikari, Navin Kumar Zhang, Yizhe Borse, Shubhankar Mangesh Cai, Hong Mayer, Frank Calidas, Balaji Pandey, Nilesh Prasad Yin, Fei Porikli, Fatih Qualcomm AI Res San Diego CA 92121 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

This demonstration showcases our innovations on efficient, accurate, and temporally consistent video semantic segmentation on mobile device. We employ our test-time unsupervised scheme, AuxAdapt, to enable the segmentation model to adapt to a given video in an online manner. More specifically, we leverage a small auxiliary network to perform weight updates and keep the large, main segmentation network frozen. This significantly reduces the computational cost of adaptation when compared to previous methods (e.g., Tent, DVP), and at the same time, prevents catastrophic forgetting. By running AuxAdapt, we can considerably improve the temporal consistency of video segmentation while maintaining the accuracy. We demonstrate how to efficiently deploy our adaptive video segmentation algorithm on a smartphone powered by a Snapdragon (R) Mobile Platform'. Rather than simply running the entire algorithm on the GPU, we adopt a crossunit deployment strategy. The main network, which will be frozen during test time, will perform inferences on a highly optimized AI accelerator unit, while the small auxiliary network, which will be updated on the fly, will run forward passes and back propagationson the GPU. Such a deployment scheme best utilizes the available processing power on the smartphone and enables real-time operation of our adaptive video segmentation algorithm. We provide example videos in supplementary material.

关键词： Performance evaluation image segmentation Technological innovation Adaptation models Semantics Graphics processing units Streaming media

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real-time Fire Detection using image and video processing

Real-time Fire Detection using Image and Video Processing

引用

Electrical Electronics and Computing Technologies (ICEECT), International conference on

作者： Rhythm Shah Ruchi Gajjar Electronics and Communication Engineering Department Institute of Technology Nirma University Ahmedabad Gujarat India

ISBN: (数字)9798350378092

ISBN: (纸本)9798350378108

There has been a rise in the frequency of fire-related calamities all over the globe, which leads to the need for an efficient fire detection system to avoid high losses or fatalities. This paper focuses on real-time fire detection techniques through image and video processing. In particular, this paper is aimed at a color detection approach that uses HSV and YCbCr color models for detecting only fire pixels along with the implementation of fire movement detection approach by comparing consecutive frames from the live feed. Overall, the study contributes to advancing fire detection methodology, highlighting the potential of image processing methods in real-time fire detection systems.

关键词： image segmentation image color analysis Motion segmentation Disasters Streaming media Filtering algorithms real-time systems Motion detection Delays Feeds

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2023 National conference on Communications, NCC 2023

2023 National Conference on Communications, NCC 2023

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2023 National conference on Communications, NCC 2023

ISBN: (纸本)9781665456258

The proceedings contain 80 papers. The topics discussed include: a lightweight deep residual attention network for single image super resolution;a heterogeneous stacking ensemble-based security framework for detecting phishing attacks;compact printed super wideband MIMO antenna with polarization diversity;power allocation in a cell-free MIMO system using reinforcement learning-based approach;time-frequency domain modified vision transformer model for detection of atrial fibrillation using multi-lead ecg signals;rate adaptation for low latency real-time video streaming;limitations of the perceptual deadband approach for haptic data compression;sniper localization using acoustic signal processing based on time of arrivals;a novel embedding architecture and score level fusion scheme for occluded image acquisition in ear biometrics system;and stationary wavelet transform based detection of aortic stenosis using seismocardiogram signal.

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