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

共 8703 条记录，以下是171-180 订阅

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Wavelet Diffusion Models are fast and scalable image Generators

Wavelet Diffusion Models are fast and scalable Image Generat...

引用

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

作者： Phung, Hao Dao, Quan Tran, Anh VinAI Res Hanoi Vietnam

ISBN: (纸本)9798350301298

Diffusion models are rising as a powerful solution for high-fidelity image generation, which exceeds GANs in quality in many circumstances. However, their slow training and inference speed is a huge bottleneck, blocking them from being used in real-time applications. A recent DiffusionGAN method significantly decreases the models' running time by reducing the number of sampling steps from thousands to several, but their speeds still largely lag behind the GAN counterparts. This paper aims to reduce the speed gap by proposing a novel wavelet-based diffusion scheme. We extract low-and-high frequency components from both image and feature levels via wavelet decomposition and adaptively handle these components for faster processing while maintaining good generation quality. Furthermore, we propose to use a reconstruction term, which effectively boosts the model training convergence. Experimental results on CelebA-HQ, CIFAR-10, LSUN-Church, and STL-10 datasets prove our solution is a stepping-stone to offering real-time and high-fidelity diffusion models. Our code and pre-trained checkpoints are available at https://***/VinAIResearch/***.

关键词： image and video synthesis and generation

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Research on video image Transmission System of Power Equipment Inspection Based on Deep Learning

Research on Video Image Transmission System of Power Equipme...

引用

2023 International conference on Big Data Mining and Information processing, BDMIP 2023

作者： Xiang, Zhiwei Hu, Meihui Li, Kai Wan, Jiao Jing, Kang State Grid Xinjlang Information & Telecommunication Company Xinjiang Urumqi830017 China State Grid Xinjlang Electric Power Co. Ltd. Xinjiang Urumqi830017 China

ISBN: (纸本)9798400709166

To accomplish the tasks that traditional manual patrol can accomplish, the autonomous patrol robot of substation must have the ability to collect images and transmit the collected images to the control center in time, so that the staff of the control center can make judgments according to the image information collected by the autonomous patrol robot. However, in the field monitoring and information interaction, the receiving delay of data such as voice and video is large, and the received data information is seriously lost, which has a great impact on the online monitoring and command function of the system. In this paper, a video image transmission system for power equipment inspection based on deep learning is proposed, which can ensure the real-time video transmission and improve the transmission quality. A video image compression framework for power equipment inspection based on CNN (Convolutional Neural Network) is proposed, which uses two neural networks at the front end of the encoder and the back end of the decoder to unite the codecs. The iterative learning algorithm proposed in this paper is used for joint training to reconstruct images with high quality. The experimental results show that the system can reduce the transmission delay time, improve the transmission quality, significantly improve the decoding quality of transmitted video, and meet the real-time requirements, providing technical guidance for improving the inspection ability of power equipment. © 2023 ACM.

关键词： Inspection

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Dynamic-Feature-Based Object Tracking Using real-time image processing 5th

Dynamic-Feature-Based Object Tracking Using Real-Time Image ...

引用

5th International conference on Data Science, Machine Learning and Applications, ICDSMLA 2023

作者： Mishra, Awakash Rengarajan Bhatt, Rahul Prince, P. Blessed Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bangalore India Department of Computer Science & Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Navgaon Manduwala Dehradun India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India

ISBN: (纸本)9789819780426

This research explores the utility of today's real-time picture processing for dynamic-characteristic-primarily based object monitoring. Notably, this painting proposes a novel tracking method that combines an actual-time, place-primarily based convolutional neural community (R-CNN) with monitoring with the detection (TbD) technique. The proposed approach is for contemporary R-CNNs to constantly come across transferring objects in video frames and then modern-day the TbD technique to generate accurate item tracking information. The proposed system is evaluated on two benchmark datasets and compared to numerous tracking methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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image Classifier Using Resource-Constrained Device and Tiny Machine Learning 1st

Image Classifier Using Resource-Constrained Device and Tiny ...

引用

1st International conference on Data Engineering and Machine Intelligence, ICDEMI 2023

作者： Vinod, K.S. Kanmani Ruby, E.D. Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (纸本)9789819776153

image classification is one of the main parts of computer vision, which is important in applications like self-driving automotives/vehicle systems. While working with image/video data it needs huge amount of resources including computing power, graphic processing units (GPU), memory, high end CPUs, etc. We can use the small microcontrollers to do the same task, by using high-end machines for training and building the model and converting the model so that they fit into microcontroller unit, by using the method called Transfer Learning. In our work, we use Arduino nano 33 BLE sense and OV7675 camera module and online machine learning framework called Edge impulse for building the model. It is found that our tiny machine learning model works well and provides a real-time solution for image classification in the resource-constrained scenario. The experimental results show that the image classifier is performing with around 100% accuracy and so has got a wide scope in real-time classification applications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Graphics processing unit

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P-YOWO: Attention-based spatiotemporal action detection method 4

P-YOWO: Attention-based spatiotemporal action detection meth...

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4th International conference on image processing and Intelligent Control, IPIC 2024

作者： Li, Jiake Xiong, Jiping Yao, Wenhao ZJNU Jinhua321000 China College of Physics and Electronic Information Engineering Zhejiang Normal University Jinhua321000 China

ISBN: (纸本)9781510682313

The task of spatiotemporal action detection plays a pivotal role in various domains such as video surveillance, medical fields, and sports analytics, necessitating real-time accuracy. One of the key challenges in spatiotemporal action detection lies in extracting temporal features. While many methods employ an additional branch for temporal feature extraction, effectively integrating the information obtained from these two branches remains a significant challenge. In this article,, we propose a new framework for real-time action detection.P-YOWO, which conducts spatial and spatiotemporal modeling through two distinct branches to achieve action classification and bounding box *** this endeavor, We improve the spatial modeling ability of the network for the current frame by improving the 2DCNN branch of the YOWO network structure. However, YOWO falls short in effectively leveraging spatial feature information. To address this limitation, we introduce a spatial attention mechanism in the fusion of branch information. This mechanism applies spatial self-attention to bolster the integration of spatial and temporal features in the spatial dimension. Through experiments conducted on the UCF101-24 dataset, our model exhibits an improvement of 4.8% in both frame-level and video-level average precision compared to the original model. The experimental results validate that our enhanced approach effectively explores spatial semantic information in feature maps, thereby augmenting the detection performance of the model. © 2024 SPIE.

关键词： Feature extraction Feature fusion Convolution Network architectures video Matrices Matrix multiplication Semantics Data modeling Modeling

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Pruning Based Improved Pedestrian Detection in video Surveillance 11

Pruning Based Improved Pedestrian Detection in Video Surveil...

引用

11th International conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Sharma, Suchi Diwakar, Manoj Gangodkar, Durgaprasad Graphic Era Deemed to Be University Dept. of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350350357

real-time object identification applications are using methods for image processing more and more as a result of advancements in computer technology. Results for pedestrian identification were improved by the use of deep learning techniques. To locate human beings quickly and precisely, this study suggests employing pruning to increase the Yolo models' efficiency in pedestrian recognition. This method would allow the device to function seamlessly when used for video surveillance. This method uses one stage detector, YOLOv5, to better suit the task situations and adjust to the real-time demands. To improve the possible model sparsity, 1 × 1 kernel weights are pruned using a pruning approach. The findings show that, following pruning, this model attains a 30% sparsity in this model, which indicates that 30% of the weight parameters in the nn. Conv2d layers of the algorithm are equal to 0. Inference time is nearly constant and soft-nms is applied for better ROI results. In addition, a thorough result and discussion are given to demonstrate the efficacy of this methodology regarding mAP, latency, energy consumption, and attained sparsity. © 2024 IEEE.

关键词： Energy utilization

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Fire Detection Using image processing in Industries 5

Fire Detection Using Image Processing in Industries

引用

5th International conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2024

作者： Kumaresan, V. Abdul Fashin, A. Aheilan, M. Azhageshwar, F. Sujay, L. Kongu Engineering College Department of Electrical and Electronics Engineering Erode India

ISBN: (纸本)9798350385649

Nowadays, fire in the workplace causes significant damage because it is not used early on due to a lack of awareness. As a result, all machines in the industry could be damaged. The most prevalent causes of fire include static electricity, overheating, error by humans, external factors, and mechanical failure. This study addresses concerns with existing solutions by incorporating functionality such as alert and detection. To get real-time detection results, the fire detection unit was created using PythonCV and an image processing model. To detect fire images, an image pre-processing technique called the Haar Cascade Classifier was used. The trained model was tested and compared with other cutting-edge methods. A big fire scale and negative photos of fire in various indoor and outdoor environments were used. The video camera has no specific requirements. The camera will keep monitoring the situation in the room as long as the device is powered ON. If the camera detects a fire, the image is evaluated using an image processing model, the buzzer sounds, and an alarm message is delivered to the user's smartphone via Email. © 2024 IEEE.

关键词： Fires

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Boosting Accuracy and Reducing time in Laser Shooting Practices: video processing Solutions for Impact Evaluation 1

引用

8th International conference on Information and Communication Technology for Intelligent Systems, ICTIS 2024

作者： Rivas-Lalaleo, David Bautista-Naranjo, Víctor Vayas-Ortega, Germania Iza-Chango, Erika Lema-Jumbo, Pamela Bran, Carlos Universidad de las Fuerzas Armadas—ESPE Sangolquí Ecuador Universidad Indoamerica Ambato Ecuador Instituto Investigación e Innovación en Electrónica Universidad Don Bosco Soyapango El Salvador

ISBN: (数字)9789819757992

ISBN: (纸本)9789819757985

This study explores the most effective method for impact measurement in laser shooting ranges, crucial for security training, accident prevention, and cost reduction. It utilizes video surveillance and image processing algorithms in Python to accurately determine laser impact locations. The research found that employing a single target per silhouette is most efficient, minimizing errors to ±1 impact and enabling heat map generation for feedback. The combination of video surveillance and image processing significantly enhances safety and reduces costs by providing real-time data. The findings advocate for the single target approach due to its precision and the comprehensive insights gained from heat maps, maintaining accuracy within a two-hit margin. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： video signal processing

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Enhanced real-time Object Detection on Customized Dataset with YOLOv9 Architecture

Enhanced Real-Time Object Detection on Customized Dataset wi...

引用

2024 IEEE International conference on Intelligent Signal processing and Effective Communication Technologies, INSPECT 2024

作者： Adithiyan, P.V. Kumar, M Ranjith Rahul, K. Nikhil, V. Annamalai, R. Amrita School of Computing Amrita Vishwa Vidyapeetham Dept. of Computer Science and Engg. Chennai India School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350379525

YOLO has developed into a primary real-time object identification platform for applications such as video surveillance systems, autonomous vehicles, and robots. This research proposes an improved real-time object recognition approach based on the YOLOv9 architecture, trained on a football-specific custom dataset. The 255 annotated images in the dataset are separated into training, validation, and test sets with an 80-15-5 split ratio;there are 204 images in the training set, 38 in the validation set, and 13 in the test set. Our gelan-c model achieves remarkable detection performance with 467 layers, 25,414,044 parameters, and 102.5 GFLOPs. Upon validation, Gelan-c shows an aggregate mAP50 of 75.9%, with mAPs ranging from 32.2% (ball) to 93.7% (goalkeeper) depending on the specified class. The impressive aspect of the Gelan-C is its 31.6 ms picture processing rate, which assures real-time performance. The methodology covers environmental setup, preparation of data, YOLOv9 architecture introduction, and experimental evaluation to demonstrate the model's football-related recognition capabilities. The suggested approach has potential applications in sports event monitoring and evaluation due to its reliability and efficacy in detecting football players, referees, and the ball. © 2024 IEEE.

关键词： image annotation

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real-time video-based Point Cloud Encoding System on a Distributed Platform 14

Real-Time Video-based Point Cloud Encoding System on a Distr...

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14th International conference on Ubiquitous and Future Networks, ICUFN 2023

作者： Kim, Yura Kim, Yong-Hwan Intelligent Image Processing Research Center Seongnam-si Korea Republic of

ISBN: (纸本)9798350335385

In 2021, the MPEG introduced the video-based point cloud compression(V-PCC) standard, achieving an excellent 3D point cloud data compression ratio. However, the high computational complexity made real-time encoding impossible. To address this issue, we designed and implemented the V-PCC encoding system in real-time, leveraging the capabilities of a distributed platform. We achieve over 30 frames per second when encoding point cloud data with the proposed V-PCC real-time system. In this paper, we describe the architecture of the proposed real-time V-PCC encoding system and present demonstration results. © 2023 IEEE.

关键词： real time systems

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