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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361-370 订阅

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The Study of Efficacy of Gaussian Mixture Model In image Tracking System Using the Canny Optical Flow Technique 4

The Study of Efficacy of Gaussian Mixture Model In Image Tra...

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4th IEEE-Nigeria-Section International conference on Disruptive Technologies for Sustainable Development (IEEE NIGERCON)

作者： Abdulkadir, Alabi Adegbola, Oluwole Abiodun Idowu, Peter Olalekan Aborisade, David Olugbenga Kwara State Polytech Dept Elect & Elect Engn Ilorin Nigeria Ladoke Akintola Univ Technol Dept Elect & Elect Engn Ogbomosho Nigeria

ISBN: (纸本)9781665479783

Detecting and tracking moving objects in a sequence of video images is an important operation in the field of computer vision. However, problems of image noise, complex object forms and motions, and real-time processing are few of the challenging facing existing flow calculation methods as they are computationally complicated and highly vulnerable to noise. Hence, this paper presents a technique less susceptible to noise and computationally efficient. Canny Optical Flow (Canny-O-Flow) was used in this study to develop a framework for recognizing and tracking moving objects in video. video datasets in avi and mp4 format were acquired from MathWorks and YouTube respectively. The obtained datasets were preprocessed by splitting the videos into numerous frames. The video dataset's rapid shift in intensity and Object's borders were discovered using Gaussian Mixture Model and Canny Edge technique, respectively. The performance of Canny-O-Flow and O-Flow techniques were investigated by compared using Accuracy, Precision, False Acceptance Rate, False Rejection Rate and processing time as the performance metrices. Additionally, the techniques were validated by t-test to compare the differences between the two techniques at 5% significance level. It showed that p=0.008 for test of significance between the Canny-O-Flow and O-Flow techniques. The developed Canny-O-Flow framework was able to recognize and track moving objects more efficiently and with less processing time than the standard technique.

关键词： Object detection video sequence canny edge detection object tracking optical flow

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Evaluation of Embedded Devices for real-time video Lane Detection 29

Evaluation of Embedded Devices for Real-Time Video Lane Dete...

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29th International conference on Mixed Design of Integrated Circuits and System (MIXDES)

作者： Podbucki, Kacper Suder, Jakub Marciniak, Tomasz Dabrowski, Adam Poznan Univ Tech Fac Automat Control Robot & Elect Engn Inst Automat Control & Robot Div Signal Proc & Elect Syst Jana Pawla II 24 PL-60965 Poznan Poland

ISBN: (数字)9788363578220

ISBN: (纸本)9781665461764

This paper presents a comparison of the performance of embedded systems processing video sequences in real time. As part of the work, practical programs for detecting lanes located on airport areas, which allow autonomous vehicles to move around the airport, were tested. The following modules were used during the tests: Raspberry Pi 4B, NVIDIA Jetson Nano, NVIDIA Jetson Xavier AGX. For modules from the NVIDIA Jetson family, the maximum performance of video stream processing depending on the resolution and the selected power mode has been checked. The results of the experiment show that NVIDIA Jetson modules have sufficient computing resources to effectively track lines based on the camera image, even in low power modes.

关键词： lane detection embedded systems video analysis microprocessor power modes NVIDIA Jetson Raspberry Pi

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Underground target location method based on positive image-based camera position and pose estimate 23

Underground target location method based on positive image-b...

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6th International conference on Signal processing and Machine Learning, SPML 2023

作者： Liu, Yi Cai, Wen-Hao Wang, Wen-Ke Lyu, Zhi-Qin Min, Zhi-Peng Gao, Yu-Xiang China

ISBN: (纸本)9798400707575

At present, the traditional underground target positioning technology is passive positioning. It needs to rely on external equipment such as communication networks, hosts and reference base stations with known position coordinates to achieve target positioning. The location system is complex and the ability to resist disasters is weak. To get rid of the problems existing in the passive positioning system, this paper proposes an underground target location method based on a positive image-based camera position and pose estimate. The method consists of two stages: an offline stage and a real-time positioning stage. In the offline stage, a number of identification signboards are arranged at known positions in the tunnel, and the coordinate position information of each identification signboard is recorded, and it is built into the identification signboard-position information database. In the real-time positioning stage, the main body of this stage is the camera, and the camera, video analysis module, and storage module storing the signboard-position information database is embedded into the embedded system, and the embedded system is carried by the positioning target. The embedded system uses the camera and video analysis module to find one of the signboards, finds the coordinate position information of the signboard in the signboard-position information database, and establishes the signboard coordinate system with the coordinate position as the origin;according to the angle The coordinate value of the point in the pixel coordinate system and the signboard coordinate system, as well as the PnP algorithm, calculate the coordinate value of the corner point in the camera coordinate system. Through the different coordinate values of the same corner point in the camera coordinate system and the signboard coordinate system, the mutual relationship between the two coordinate systems is solved by inversion, that is, the camera extrinsic parameters. The relative pose of the ca

关键词： Cameras

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FrameFire: Enabling Efficient Spiking Neural Network Inference for video Segmentation 5

FrameFire: Enabling Efficient Spiking Neural Network Inferen...

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5th IEEE International conference on Artificial Intelligence Circuits and Systems, AICAS 2023

作者： Chen, Qinyu Sun, Congyi Gao, Chang Fang, Xinyuan Luan, Haitao

ISBN: (纸本)9798350332674

Fast video recognition is essential for real-time scenarios, e.g., autonomous driving. However, applying existing Deep Neural Networks (DNNs) to individual high-resolution images is expensive due to large model sizes. Spiking Neural Networks (SNNs) are developed as a promising alternative to DNNs due to their more realistic brain-inspired computing models. SNNs have sparse neuron firing over time, i.e., spatio-temporal sparsity;thus they are useful to enable energy-efficient computation. However, exploiting the spatio-temporal sparsity of SNNs in hardware leads to unpredictable and unbalanced workloads, degrading energy efficiency. In this work, we, therefore, propose an SNN accelerator called FrameFire for efficient video processing. We introduce a Keyframe-dominated Workload Balance Schedule (KWBS) method. It accelerates the image recognition network with sparse keyframes, then records and analyzes the current workload distribution on hardware to facilitate scheduling workloads in subsequent frames. FrameFire is implemented on a Xilinx XC7Z035 FPGA and verified by video segmentation tasks. The results show that the throughput is improved by 1.7× with the KWBS method. FrameFire achieved 1.04 KFPS throughput and 1.15 mJ/frame recognition energy. © 2023 IEEE.

关键词： Deep neural networks

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Cascade Attentional Fusion for Unsupervised Domain Adaptation on Multi-Modal Egocentric video Analysis

Cascade Attentional Fusion for Unsupervised Domain Adaptatio...

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2023 International conference on image, Signal processing, and Pattern Recognition, ISPP 2023

作者： Liu, Xianyuan Zhang, Sanxing Lei, Tao Jiang, Ping Institute of Optical and Electronics Chinese Academy of Sciences Chengdu610209 China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing101408 China University of Chinese Academy of Sciences Beijing100049 China

ISBN: (纸本)9781510666351

Supervised learning models require a large-scale dataset for more effective model training. However, a large-scale dataset requires time-consuming data collection and highly complicated preprocessing, posing a significant challenge for researchers. Therefore, creating a large-scale dataset for each practical application is unachievable in a real-world setting. In this case, unsupervised domain adaptation is crucial for practical applications, as it can enable models to enhance their performance on unlabeled data by training on labeled data. For multi-modal egocentric video analysis, some models have used unsupervised domain adaptation and achieved outstanding performance. However, they use either early or late fusion, ignoring the correlation in and between multi-modal inputs. Therefore, this paper investigates different fusion architecture and proposes a cascade attentional fusion to improve the feature representation for unsupervised domain adaptation on multi-modal egocentric video analysis. First, we propose a cascade fusion architecture for increased audio signal reuse. Then, we propose a temporal-spatial attention mechanism for highlighting spatio-temporal feature representations. Third, we propose a novel cascade attentional fusion method for multi-modal egocentric video data fusion by incorporating the architecture and attention model described previously. In addition, we study the ways for integrating different attentions. Finally, we propose an adversarial domain alignment model that incorporates the proposed fusion for unsupervised domain adaptation on multi-modal egocentric video analysis, reaching state-of-the-art performance on the public dataset. © 2023 SPIE.

关键词： Data fusion

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VIhanceD: Efficient video Super Resolution for Edge Devices 2

VIhanceD: Efficient Video Super Resolution for Edge Devices

引用

2nd Edition of IEEE Delhi Section Owned conference, DELCON 2023

作者： Papanai, Ashish Babbar, Siddharth Pandey, Anshuman Kathuria, Harshit Sharma, Alok Kumar Gupta, Namita Maharaja Agrasen Institute of Technology Department of Computer Science and Engineering Delhi India

ISBN: (纸本)9798350322064

video Super Resolution (VSR) aims to generate high-resolution (HR) frames from corresponding low-resolution (LR) frames. It draws a stark contrast from single image super-resolution (SISR) because of its high temporal dependency on misaligned supporting frames. The existing methods involve using RNNs to learn the temporal dependency by using other networks (CNNs, GANs) for predicting neighboring pixels. Due to the memory and processing constraints and the inference time required for up-scaling LR frames, a wide variety of VSR techniques cannot be applied to mid-range and budget mobile devices. This paper presents VIhanceD, a real-time sliding window-based network that can operate on budget smartphones and laptops while producing cutting-edge results on various video datasets. Our approaches include both spatial and temporal dependencies to make the up-scaled HR frames coherent and free of motion distortions. We focus on enhancing the user experience in areas with internet restrictions due to social, political, and geographical limitations. The mobile app (and PC client) provides a continuous stream of HR frames without buffering. We obtained 33.9 PSNR on REDS Dataset with a single frame inference time of 23.6 ms demonstrating state-of-the-art performance. Our subsequent experiments on VIMEO-90K dataset demonstrates that the suggested method is generalizable and works on natural video frames and textual data, making it suitable for infotainment and multimedia. © 2023 IEEE.

关键词： Optical flows

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Accelerating Traditional Object Detection Methods on Sophisticated Embedded Systems 4th

Accelerating Traditional Object Detection Methods on Sophist...

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4th International conference on Intelligent Systems and Pattern Recognition-ISPR

作者： Sehairi, Kamal Bouhafs, Abdelkader Boucherit, Ibtissam Mama Bouwmans, Thierry Chouireb, Fatima Ecole Normale Super Laghouat Dept Phys Lab Appl Sci & Didact Laghouat Algeria Univ Amar Telidji Laghouat Dept Elect Telecommun Signals & Syst Lab Laghouat Algeria Univ La Rochelle Dept Comp Sci Lab Math Image & Applicat La Rochelle France

ISBN: (纸本)9783031821523;9783031821530

Identifying and locating objects in images and videos, including elements like traffic signs, vehicles, buildings, and people, constitutes a fundamental and demanding task in computer vision, known as object detection. Due to the higher computing complexity of this technique and the large amount of data carried by the video signal, it is nearly impossible for ordinary general-purpose processors GPPs or CPUs to run these techniques in real-time, especially for embedded systems applications. Therefore, special hardware that can acquire, control, or execute in parallel is required. These specialized hardware systems include Digital Signal Processors DSPs, Field Programmable Gate Arrays FPGAs, Visual processing Units VPUs, Tensor processing Units TPUs, Neural processing Units NPUs or Graphics processing Units GPUs. This work presents the benefits of accelerating traditional object detection methods on a high-end embedded system, the Jetson Nano Developer Kit. This single computer board is equipped with the Tegra K1 System on Chip SoC, which is composed of a quad-core ARM A15 and 192 cores of Kepler-embedded GPU. Computing acceleration was ensured via the use of the CUDA OpenCV library for both the Histogram of Oriented Gradients HOG and the Haar Cascade Classifier. For VGA resolution, results reveal that the GPU implementation on this embedded system is 1.4x faster than the CPU for the HOG method and 2x for the Haar Cascade Classifier method.

关键词： Object detection Machine Learning Haar Cascade HOG Local feature descriptors GPU implementation OpenCV CUDA SoC Tegra X1 Jetson Nano

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Deep Learning Based Low Light Enhancements for Advanced Driver-Assistance Systems at Night 17

Deep Learning Based Low Light Enhancements for Advanced Driv...

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17th IEEE International conference on Industrial and Information Systems, ICIIS 2023

作者： Ranasinghe, Pamudu Muthukuda, Dilshan Morapitiya, Primash Dissanayake, Maheshi B Lakmal, H.K.I.S. University of Peradeniya Dept. Electrical & Electronic Eng. Peradeniya Sri Lanka

ISBN: (纸本)9798350323634

In the modern automobile industry, full/partial autonomous driving has become a common phenomenon. These systems heavily rely on sensors, both visual and non-visual, such as cameras and LIDAR, to interpret their surroundings and aid the decision-making process. Recently, visual-only sensing devices, mainly cameras, have emerged as the primary sensor for obtaining an immersive feeling in autonomous driving. However, the performance of such systems is heavily constrained by fluctuating light intensity and shadows. The primary goal of the presented research is to develop an effective low-light image enhancement system that improves the perception and inter-pretability of images taken in low-light driving environments. The developed image enhancement architecture demonstrates remarkable capabilities, surpassing existing models in terms of real-time suitability, PSNR (Peak Signal-to-Noise Ratio), and SSIM (Structural Similarity Index) values. Additionally, the model has been specifically designed to have low resource usage, making it an ideal solution for deployment on low-end devices. With significantly lower inference time, the model is exceptionally well-suited for real-time operation in autonomous driving applications. Furthermore, this architecture provides an efficient and effective solution not only for autonomous driving but also for a range of applications in video/image processing and computer vision, such as surveillance systems, medical imaging, astronomy, underwater imaging, and the mining industry. Finally, the relatively short training time makes it an attractive option for researchers and developers. Overall, this architecture represents a significant advancement in low-light image enhancement and autonomous vehicles. A demo of the system and the source code of the proposed LLE_UNET can be found on https://***/pamudu123ILow_Light_image_Enhancement. © 2023 IEEE.

关键词： image enhancement

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real-ESRGAN in Neural Rendering Frameworks for High-Fidelity video Upscaling

Real-ESRGAN in Neural Rendering Frameworks for High-Fidelity...

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Advanced Computing Technologies (ICoACT), International conference on

作者： Sarthak Das Shivani Kamboj Department of Mathematics (of Aff.) Chandigarh University Chandigarh India

ISBN: (数字)9798331509859

ISBN: (纸本)9798331509866

As digital video content continues to grow, the need for enhanced visual quality has become increasingly important. However, utilizing the real-ESRGAN video module can often lead to issues such as frame skipping and inconsistent enhancement results. This paper tackles these issues by introducing a novel approach that enhances individual frames of a video separately. By converting the original video into distinct frames and applying real-ESRGAN for enhancement, we ensure that each frame is treated individually, thereby eliminating frame skips and achieving uniform enhancement across the video. After processing, the frames are reassembled into a cohesive video output. Our extensive evaluations reveal significant improvements in visual fidelity compared to conventional enhancement techniques. This research highlights the promise of integrating neural rendering methods in various fields, including film, gaming, and online streaming, and provides valuable contributions to the evolving landscape of video processing.

关键词： Measurement Visualization Laplace equations Streaming media Media Rendering (computer graphics) real-time systems Computational efficiency image restoration image reconstruction

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An Ensemble Approach for Moving Vehicle Detection and Tracking by Using Ni Vision Module 7th

An Ensemble Approach for Moving Vehicle Detection and Tracki...

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7th International conference on Computer Vision and image processing, CVIP 2022

作者： Singh, Pankaj Pratap Ramchiary, Preeti Bora, Jahirul Islam Bhuyan, Rakesh Prasad, Shitala Department of Computer Science and Engineering Central Institute of Technology Kokrajhar Assam Kokrajhar India Institute for Infocomm Research A*Star Singapore Singapore

ISBN: (纸本)9783031314162

In the recent years, surveillance systems and video monitoring have been largely used for the management of traffic. Acquired images and video clips from the road traffic can be utilized in the Lab VIEW program environment. LabVIEW Vision Assistant is focusing on to discover the moving vehicles. This approach finds a formation of resemblance in the frames for vehicle and non-vehicle objects, while the vehicles tracking progress through image sequences. For improving the adaptive background mixture model, there is indeed of background subtraction method. In addition, it constructs the system more precisely with rapid learning also. Therefore, its performance shows the adaptability on the occurrence of any real time videos. This evolve system robustly detect the vehicles on resolving the background objects which help to track the vehicles effectively. The various types of attributes related to moving vehicles are extracted which are used in feature extraction techniques for tracking the vehicles. The extracted features utilize in the module of LabVIEW environment and Vision Assistant module works mainly in the detection of moving vehicles objects. This proposed work can help in reducing the cost of traffic monitoring systems and real automation of traffic observation systems. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： image enhancement

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