检索结果-内蒙古大学图书馆

7th International conference on Automation Electronics and Electrical Engineering

作者： Chen, Xinghao Beihang Univ Sch Elect Informat Engn Beijing Peoples R China

ISBN: (纸本)9798350377040;9798350377033

With the continuous progress of image processing and machine vision technology, the demand for efficient and real-time processing is becoming more and more prominent, especially in the field of high-noise image processing. In this study, an adaptive Gaussian filtering algorithm is proposed, which is implemented based on FPGA and aims to improve the computational efficiency and real-time performance of the image processing system. Compared with the traditional fixed-weight filter, this algorithm is able to dynamically adjust the filtering parameters according to different noise environments, effectively balancing noise suppression and image detail retention. We coded the algorithm using Verilog hardware description language and verified it on PYNQ-Z2 FPGA platform. The experimental results show that the adaptive algorithm outperforms the fixed-weight filtering method in terms of performance, especially in terms of noise suppression and detail preservation. Meanwhile, the FPGA hardware implements the reduction of filtering delay and optimization of resource consumption, making it well suited for real-time applications. This study demonstrates the promise of FPGA adaptive filtering for applications in medical imaging, remote sensing, and intelligent surveillance, which have stringent requirements for high-performance and high-efficiency processing. This research provides new hardware solutions for real-time, high-quality image processing in constrained environments.

关键词： FPGA image denoising gaussian filtering adaptive algorithm

来源：评论

学校读者我要写书评

暂无评论

A Simulation of Energy Optimized Distributed video processing on 28 GHz Network 27

A Simulation of Energy Optimized Distributed Video Processin...

引用

27th conference on Innovation in Clouds, Internet and Networks (ICIN)

作者： Techasarntikul, Nattaon Shimonishi, Hideyuki Murata, Masayuki Osaka Univ Osaka Japan

ISBN: (纸本)9798350393767;9798350393774

Transmitting a high data rate video to the cloud for real-time processing purpose requires minimizing the latency, maximizing the application requirements, and optimizing power consumption for the entire system. In this study, we employed a distributed video processing model for an object detection task, assuming that video streams are captured by robots operating in the licensed 28 GHz Milliwave network, ensuring the stability of video uploads. Through the optimization of power consumption, the system efficiently allocated video analysis frames to appropriate devices, resulting in an 18% decrease in overall power usage.

关键词： distributed video processing 28 GHz Millimeter-wave object detection

来源：评论

学校读者我要写书评

暂无评论

A*: Atrous Spatial Temporal Action Recognition for real time ApplicationsA*: Atrous Spatial Temporal Action Recognition for real time Applications

A*: Atrous Spatial Temporal Action Recognition for Real Time...

引用

IEEE/CVF Winter conference on Applications of Computer Vision (WACV)

作者： Kim, Myeongjun Spinola, Federica Benz, Philipp Kim, Tae-hoon Deeping Source Inc Seoul South Korea

ISBN: (纸本)9798350318920;9798350318937

Deep learning has become a popular tool across various fields and is increasingly being integrated into real-world applications such as autonomous driving cars and surveillance cameras. One area of active research is recognizing human actions, including identifying unsafe or abnormal behaviors. Temporal information is crucial for action recognition tasks. Global context, as well as the target person, are also important for judging human behaviors. However, larger networks that can capture all of these features face difficulties operating in real-time. To address these issues, we propose A*: Atrous Spatial Temporal Action Recognition for real time Applications. A* includes four modules aimed at improving action detection networks. First, we introduce a Low-Level Feature Aggregation module. Second, we propose the Atrous Spatio-Temporal Pyramid Pooling module. Third, we suggest to fuse all extracted image and video features in an image-video Feature Fusion module. Finally, we integrate the Proxy Anchor Loss for action features into the loss function. We evaluate A* on three common action detection benchmarks, and achieve state-of-the-art performance on JHMDB and UCF101-24, while staying competitive on AVA. Furthermore, we demonstrate that A* can achieve real-time inference speeds of 33 FPS, making it suitable for real-world applications.

关键词： Algorithms video recognition and understanding

来源：评论

学校读者我要写书评

暂无评论

Vehicle video stabilization algorithm based on grid motion statistics and adaptive Kalman filtering

引用

SIGNAL image AND video processing 2024年第2期18卷 1969-1981页

作者： Li, Chengcheng YuanTian Ma, Lisen Jia, Yunhong Bi, Yueqi China Coal Res Inst Beijing 100013 Peoples R China Shanxi Tiandi Coal Min Machinery Co Ltd Taiyuan 030006 Peoples R China CCTEG Taiyuan Res Inst Co Ltd Taiyuan 030006 Peoples R China

Owing to the impact of vibration on the carrier of a vehicle-mounted camera, video is shaking, resulting in decreased or failed recognition accuracy based on visual-target detection. To solve this problem, a video stabilization algorithm based on grid motion statistics and an adaptive Kalman filter is proposed. Two important processes in video stabilization are motion estimation and motion smoothing. In the motion estimation stage, we adopt an erroneous matching removal algorithm that integrates grid motion statistics (GMS) to enhance the accuracy of motion estimation while reducing the matching time, further meeting the real-time and precision requirements of vehicle-mounted video stabilization. In the motion smoothing stage, we adaptively update the measurement noise covariance R in the adaptive Kalman filter based on the camera shake level, further improving the accuracy of motion smoothing under the condition of ensuring filter convergence. Finally, we compensate for the motion based on the relationship between the pre- and postsmooth motion trajectories, generating a stable video sequence. Experimental results demonstrate that the proposed algorithm exhibits good stability and effectiveness in vehicle-mounted video stabilization.

关键词： video stabilization ORB Grid motion statistics Adaptive Kalman filtering PSNR

来源：评论

学校读者我要写书评

暂无评论

Intelligent identification of concrete uniformity based on dynamic mixing

引用

SIGNAL image AND video processing 2024年第1期18卷 427-436页

作者： Liu, Mingtang Li, Bin Yue, Shuang Du, Yuying Xu, Jie North China Univ Water Resources & Elect Power Zhengzhou Peoples R China

The uniformity of concrete is an important reference for the maturity of concrete, and is also closely related to the quality and safety of the product. In order to analyze the performance of concrete during the mixing process, in view of the problem that there is no scientific and effective method to detect the uniformity of concrete during the mixing process, this paper proposes an intelligent identification method for the uniformity of concrete based on dynamic mixing. The method measures the surface of concrete fluid through computer graphical modeling, applies mathematical and computational models to the interaction of fluid dynamics, and uses the computer to independently judge the characteristics of the concrete fluid state under non-artificial conditions, thereby obtaining the state of concrete uniformity. The experimental results show that the average accuracy of the intelligent identification method of concrete uniformity based on dynamic mixing is 97.14%, and the real-time monitoring speed reaches 12FPS/S, which has important reference significance for the real-time state detection of concrete. The identification accuracy and monitoring speed can both meet the actual monitoring needs of the concrete mixing station.

关键词： Fluid measurement Uniformity image processing Mathematical modeling Feature extraction

来源：评论

学校读者我要写书评

暂无评论

Test-time Training for Matching-based video Object Segmentation 37

Test-time Training for Matching-based Video Object Segmentat...

引用

37th conference on Neural Information processing Systems (NeurIPS)

作者： Bertrand, Juliette Kordopatis-Zilos, Giorgos Kalantidis, Yannis Tolias, Giorgos Czech Tech Univ VRG FEE Prague Czech Republic NAVER LABS Europe Meylan France

ISBN: (纸本)9781713899921

The video object segmentation (VOS) task involves the segmentation of an object over time based on a single initial mask. Current state-of-the-art approaches use a memory of previously processed frames and rely on matching to estimate segmentation masks of subsequent frames. Lacking any adaptation mechanism, such methods are prone to test-time distribution shifts. This work focuses on matching-based VOS under distribution shifts such as video corruptions, stylization, and sim-to-real transfer. We explore test-time training strategies that are agnostic to the specific task as well as strategies that are designed specifically for VOS. This includes a variant based on mask cycle consistency tailored to matching-based VOS methods. The experimental results on common benchmarks demonstrate that the proposed test-time training yields significant improvements in performance. In particular for the sim-to-real scenario and despite using only a single test video, our approach manages to recover a substantial portion of the performance gain achieved through training on real videos. Additionally, we introduce DAVIS-C, an augmented version of the popular DAVIS test set, featuring extreme distribution shifts like image-/video-level corruptions and stylizations. Our results illustrate that test-time training enhances performance even in these challenging cases. Project page: https://***/test-time-training-vos/

关键词： image segmentation

来源：评论

学校读者我要写书评

暂无评论

Task-Oriented video Compressive Streaming for real-time Semantic Segmentation

引用

IEEE TRANSACTIONS ON MOBILE COMPUTING 2024年第12期23卷 14396-14413页

作者： Xiao, Xuedou Zuo, Yingying Yan, Mingxuan Wang, Wei He, Jianhua Zhang, Qian Wuhan Univ Technol Sch Nav Wuhan 430062 Peoples R China Huazhong Univ Sci & Technol Sch Elect Informat & Commun Wuhan 430074 Peoples R China Essex Univ Sch Comp Sci & Elect Engn Colchester CO4 3SQ England Hong Kong Univ Sci & Technol Dept Comp Sci & Engn Clear Water Bay Hong Kong Peoples R China

real-time semantic segmentation (SS) is a major task for various vision-based applications such as self-driving. Due to the limited computing resources and stringent performance requirements, streaming videos from camera-embedded mobile devices to edge servers for SS is a promising approach. While there are increasing efforts on task-oriented video compression, most SS-applicable algorithms apply more uniform compression, as the sensitive regions are less obvious and concentrated. Such processing results in low compression performance and significantly limits the capacity of edge servers supporting real-time SS. In this paper, we propose STAC, a novel task-oriented DNN-driven video compressive streaming algorithm tailed for SS, to strike accuracy-bitrate balance and adapt to time-varying bandwidth. It exploits DNN's gradients as sensitivity metrics for fine-grained spatial adaptive compression and includes a temporal adaptive scheme that integrates spatial adaptation with predictive coding. Furthermore, we design a new bandwidth-aware neural network, serving as a compatible configuration tuner to fit time-varying bandwidth and content. STAC is evaluated in a system with a commodity mobile device and an edge server with real-world network traces. Experiments show that STAC can save up to 63.7-75.2% of bandwidth or improve accuracy by 3.1-9.5% compared to state-of-the-art algorithms, while capable of adapting to time-varying bandwidth.

关键词： image coding Bandwidth Streaming media Semantic segmentation Accuracy Servers Predictive coding Adaptive streaming DNN-driven compression edge computing semantic segmentation

来源：评论

学校读者我要写书评

暂无评论

A Fast Texture-Based 8K Intra-Partitioning Algorithm for Versatile video Coding (VVC)

A Fast Texture-Based 8K Intra-Partitioning Algorithm for Ver...

引用

2024 conference on Visual Communications and image processing

作者： Simsek, Altug Dundar, Gunhan Bogazici Univ Elect & Elect Engn Dept Istanbul Turkiye

ISBN: (纸本)9798331529543;9798331529550

Versatile video Coding (VVC), standardized in 2022 as ITU-T Recommendation H.266, ISO/IEC 23090-3 and MPEG-I Part3, is the latest block-based hybrid video coding standard. It defines many tools to increase compression efficiency while maintaining the same quality level. The tradeoff is the computational complexity. The intra-coding loop of VVC is computationally very complex due to its nature of iteratively trying all possible Quad-Tree Multitype-Tree (QTMT) partitioning alternatives starting from 128x128 Coding Tree Unit (CTU) size and going down to 4x4 min Coding Unit (CU) size. For camera-taken real-world broadcast video, compared to smaller resolutions, the objects in 8K are larger with respect to the fixed CTU size of VVC. As a result, for such 8K video, less detailed coding in the VVC QTMT may be enough for practical purposes. In this paper, we define a new fast intra-partitioning algorithm for 8K video and compare its performance with Common Test Conditions (CTC) All-Intra (AI) configuration based on the compression efficiency (bits), quality (Y-PSNR, SSIM and MS-SSIM metrics), and computational complexity (runtime). We observe 81.61% runtime gain with only 2.99% increase in bitrate, 0.1339 dB decrease in Y-PSNR, 0.0022 decrease in SSIM and 0.0007 decrease in MS-SSIM on average. This is a remarkable gain in complexity with a limited effect on efficiency and quality.

关键词： VVC H.266 versatile video coding intra-coding CU partitioning texture-based partitioning 8K

来源：评论

学校读者我要写书评

暂无评论

Design of a 3D Digital Product Based on DSP real time video Transmission 1

Design of a 3D Digital Product Based on DSP Real Time Video ...

引用

1st IEEE International conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics, AIKIIE 2023

作者： Liu, Yuge Kim, KieSu College of Design Silla University Busan Korea Republic of

ISBN: (纸本)9798350316469

image recognition and processing technology is an important application direction of artificial intelligence technology. With the growth of demand for various types of video intelligent analysis, the importance of using multiple models and artificial intelligence technology for image recognition and video analysis has become increasingly prominent. This article delves into the multi object tracking technology in image and video processing, and based on image object detection and recognition algorithms and DSP technology, real time tracking of products in the production workshop, combined with 3D hybrid modeling technology to create corresponding product models, digitize the produced products, and provide feedback on specific product data. Providing data and graphics support for later analysis of product conformity and understanding of product conditions, improving workshop production efficiency. © 2023 IEEE.

关键词： 3D modeling DSP technology image recognition product digitization

来源：评论

学校读者我要写书评

暂无评论

Enhancing Safety and Workover Efficiency Through real- time Scale-Adaptive video Detection of Critical Objects at the Wellsite

引用

SPE JOURNAL 2024年第8期29卷 3982-3999页

作者： Zhang, Kai Song, Zewen Xia, Xiaolong Zhang, Liming Yang, Yongfei Sun, Hai Yao, Jun Zhang, Huaqing Zhang, Yue Feng, Gaocheng Liu, Chen China Univ Petr East China Qingdao Peoples R China Qingdao Univ Technol Qingdao Peoples R China CNOOC EnerTech Drilling & Prod Co Tianjin Peoples R China CNOOC Res Inst Ltd Beijing Peoples R China

The wellsite serves as the fundamental unit in the development of oil and gas fields, functioning as a hub for the production activities, with workover operations being a critical means to ensure production continuity. In addition, it plays a crucial role in environmental protection, preventing oil and gas leakage and pollution. Various pieces of mechanical equipment deployed at the wellsite are essential for tasks such as oil and gas extraction and well repair operations, holding a pivotal position in oil- and gasfield development. Consequently, intelligent wellsite implementation necessitates a primary focus on monitoring mechanical equipment, with video emerging as a vital form of multisource information at the wellsite. While existing research on wellsite video monitoring predominantly addresses system and data transmission issues, it falls short in addressing the challenges of real- time assessment and early warning in intelligent wellsite operations. This study introduces a method for identifying critical targets at the wellsite based on a scale- adaptive network. The model employs a multiscale fusion network to extract different image features and semantic features at various scales, facilitating their fusion. The processing of wellsite video images occurs in multiple stages, outputting predicted box locations and category information, enabling the localization and recognition of critical objects at the wellsite. Unlike traditional deep convolutional object detection methods, this model incorporates a parameter- free attention mechanism, enhancing the accurate feature learning of small targets during the extraction process and addressing the issue of multiscale imbalance. The experimental results validate the robust performance of the method, surpassing the latest one- stage object detection models and mainstream loss function methods. Comparative experiments demonstrate a 9.22% improvement in mean average precision (mAP) compared with YOLOv8, establishing th

关键词： deep learning artificial intelligence detection wellsite machine learning operation information neural network real time system algorithm

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：