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检索条件"任意字段=Conference on Real-Time Image and Video Processing 2011"

共 8687 条记录，以下是71-80 订阅

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SYNTHMANTICLIDAR: A SYNTHETIC DATASET FOR SEMANTIC SEGMENTATION ON LIDAR IMAGING 31

SYNTHMANTICLIDAR: A SYNTHETIC DATASET FOR SEMANTIC SEGMENTAT...

引用

2024 International conference on image processing

作者： Montalvo, Javier Carballeira, Pablo Garcia-Martin, Alvaro Univ Autonoma Madrid Video Proc & Understanding Lab Madrid Spain

ISBN: (纸本)9798350349405;9798350349399

Semantic segmentation on LiDAR imaging is increasingly gaining attention, as it can provide useful knowledge for perception systems and potential for autonomous driving. However, collecting and labeling real LiDAR data is an expensive and time-consuming task. While datasets such as SemanticKITTI [1] have been manually collected and labeled, the introduction of simulation tools such as CARLA [2], has enabled the creation of synthetic datasets on demand. In this work, we present a modified CARLA simulator designed with LiDAR semantic segmentation in mind, with new classes, more consistent object labeling with their counterparts from real datasets such as SemanticKITTI, and the possibility to adjust the object class distribution. Using this tool, we have generated SynthmanticLiDAR, a synthetic dataset for semantic segmentation on LiDAR imaging, designed to be similar to SemanticKITTI, and we evaluate its contribution to the training process of different semantic segmentation algorithms by using a naive transfer learning approach. Our results show that incorporating SynthmanticLiDAR into the training process improves the overall performance of tested algorithms, proving the usefulness of our dataset, and therefore, our adapted CARLA simulator. The dataset and simulator are available in https://***/vpulab/SynthmanticLiDAR.

关键词： Dataset LiDAR Segmentation Simulator

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Research on video Object Detection Based on real-time Linux 6

Research on Video Object Detection Based on Real-Time Linux

引用

6th IEEE International conference on Power, Intelligent Computing and Systems, ICPICS 2024

作者： Yang, Jiying Long, Qi Zhu, Xiaoyun Yang, Yuan Yunnan Open University Yunnan Province Kunming China

ISBN: (纸本)9798350374315

Object detection technology is an important research content in the field of computer vision, and it is one of the important basic technologies for understanding image content. real-time operating system refers to the operating system that can complete the processing of system request tasks within a specified time, and can provide timely response and high reliability are its main characteristics. Since the video-based target detection algorithm has high requirements on computing power and real-time performance, this paper proposes to deploy the target detection algorithm on the real-time operating system. The experiment verifies that the characteristics of the real-time operating system can improve the real-time performance of the target detection algorithm. Aiming at the hardware system of the industrial computer used in this paper, the principle and construction process of Xenomai real-time operating system are analyzed, and the scheme of building Linux+Xenomai real-time operating system on the industrial computer is proposed. Aiming at the application scenario with stable background and single target, the object detection algorithm based on image processing is studied. Based on background difference method and three-frame difference method, an improved algorithm based on adaptive detection window of target region is proposed. Experimental results show that the improved algorithm has better real-time performance than the basic algorithm. Aiming at the application scenario with complex background and multiple targets, the object detection algorithm based on deep learning is studied, and the full convolutional neural network in Dlib machine learning library is selected for research and implementation. According to the hardware and system environment of this paper, a computational scale estimation method of the total convolutional neural network is proposed, and a method of deploying the network model trained in the GPU environment in the real-time operating system e

关键词： Convolutional neural networks

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Implemention of image processing Platform on Low-cost ZedBoard FPGA Using Xilinx-Vivado

Implemention of Image Processing Platform on Low-cost ZedBoa...

引用

2024 International conference on Advances in Electrical and Communication Technologies, ICAECOT 2024

作者： Baali, Mehdi Messaoudi, Kamel Bourbia, Nadjla Bourennane, El-Bay Souk-Ahras Algeria Mentouri Brothers University Constantine 1 Laboratory for the Study of Electronic Materials for Medical Applications Algeria ImVia Laboratory Burgundy University Dijon France

ISBN: (纸本)9798350353754

The execution of complex functions and high computational tasks remains a major obstacle to efficient and transparent real-time processing. Overcoming these challenges is essential to unlocking the full potential of image and video processing technologies, and enabling their widespread adoption across diverse industries and applications. The main objective of this paper is to design an integrated image processing system based on ARM processor. This system aims to perform feature extraction and facilitate the high-speed of data transmission. The implementation results highlight the successful use of system resources, showing efficient allocation of hardware components and computing resources. Through careful design and optimization, we have achieved efficient use of available resources, ensuring that hardware IPs and associated algorithms are effectively exploited to achieve optimum performance. © 2024 IEEE.

关键词： video signal processing

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Dynamic Target Tracking Method of Surveillance video Based on image processing 3

Dynamic Target Tracking Method of Surveillance Video Based o...

引用

3rd Asia-Pacific conference on Communications Technology and Computer Science (ACCTCS)

作者： Chen, Yiping Yuan, Fengshan Guangzhou Inst Sci & Technol Dept Comp Sci & Engn Guangzhou 510540 Peoples R China

ISBN: (纸本)9798350310801

In surveillance video, target tracking is an important part. Based on image processing technology, this paper studies a real-time and effective method to collect and recognize camera motion information. Firstly, the influence of visual dead angle and illumination on recognition is analyzed. Secondly, according to the characteristic of background light intensity, the corresponding algorithm is designed to realize the positioning and tracking control strategy of the target and surrounding environment scenery. Finally, the correctness of the method is verified by MATLAB simulation software, so as to obtain a better and scalable scheme, which is more economical and feasible after the occlusion rate is minimized.

关键词： image processing Surveillance video Dynamic Target Tracking Method

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Intelligent video surveillance technology based on Mean Shift 3

Intelligent video surveillance technology based on Mean Shif...

引用

3rd International conference on Signal image processing and Communication, ICSIPC 2023

作者： Xiaoqing, Xue Bo, Chen Ting, Liu School of Physics & Electronic Engineering Xianyang Normal University Shaanxi Xianyang712000 China

ISBN: (纸本)9781510670945

In order to monitor the video state changes of safe human settlements in real time, a Mean Shift algorithm is proposed. The video monitoring images collected in real time were preprocessed with enhancement and denoising, and the gray distribution was extracted to form the feature matrix of the image. The monitoring images collected in real time by the monitoring system and the monitoring images in normal state were classified by Mean Shift algorithm. Experiments show that Mean Shift algorithm has fast convergence speed and high accuracy, and can effectively screen out abnormal images and improve retrieval efficiency. © 2023 SPIE.

关键词： image enhancement

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Efficient Fog Removal Technique For video processing 2

Efficient Fog Removal Technique For Video Processing

引用

2nd IEEE International conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2024

作者： Sai Krishna, J.V.N. Deepa, K. Amrita Vishwa Vidyapeetham Department of Electrical and Electronics Engineering Bangalore India

ISBN: (纸本)9798350360523

Foggy weather conditions frequently reduce visibility and degrade video stream quality, affecting a variety of real-world applications such as surveillance, autonomous navigation, and outdoor video recording. This paper proposes an efficient method for defogging video sequences, which will improve scene visibility and visual clarity. The proposed technique successfully removes atmospheric haze from subsequent frames in real-time by combining temporal consistency and image de-hazing algorithms. The incorporation of the dark channel prior, which accurately measures the amount of haze or fog in each frame, is a key component of the method. By leveraging the inherent statistical properties of haze-free regions, the dark channel prior enables precise transmission map estimation, facilitating efficient haze removal. Additionally, motion compensation and fusion techniques are employed to maintain temporal coherence and guarantee consistent fog removal across frames. To evaluate the efficacy of the suggested technique, a number of experiment scenarios were conducted on a variety of foggy video sequences captured in a variety of weather conditions. The results demonstrate that the suggested method successfully removes haze while keeping important image details and colors, leading to increased visual clarity. Analysis in comparison to state-of-the-art fog removal techniques shows the approach's superior real time performance and quality. Visibility in foggy video sequences is significantly improved by the suggested efficient fog removal technique that uses video processing. Because it is suitable for applications that require immediate fog clearance, like live video surveillance and autonomous vehicles. © 2024 IEEE.

关键词： Fog

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TPC: Test-time Procrustes Calibration for Diffusion-based Human image Animation 38

TPC: Test-time Procrustes Calibration for Diffusion-based Hu...

引用

38th conference on Neural Information processing Systems, NeurIPS 2024

作者： Yoon, Sunjae Koo, Gwanhyeong Lee, Younghwan Yoo, Chang D. Korea Republic of

Human image animation aims to generate a human motion video from the inputs of a reference human image and a target motion video. Current diffusion-based image animation systems exhibit high precision in transferring human identity into targeted motion, yet they still exhibit irregular quality in their outputs. Their optimal precision is achieved only when the physical compositions (i.e., scale and rotation) of the human shapes in the reference image and target pose frame are aligned. In the absence of such alignment, there is a noticeable decline in fidelity and consistency. Especially, in real-world environments, this compositional misalignment commonly occurs, posing significant challenges to the practical usage of current systems. To this end, we propose Test-time Procrustes Calibration (TPC), which enhances the robustness of diffusion-based image animation systems by maintaining optimal performance even when faced with compositional misalignment, effectively addressing real-world scenarios. The TPC provides a calibrated reference image for the diffusion model, enhancing its capability to understand the correspondence between human shapes in the reference and target images. Our method is simple and can be applied to any diffusion-based image animation system in a model-agnostic manner, improving the effectiveness at test time without additional training. © 2024 Neural information processing systems foundation. All rights reserved.

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FPGA-based Moving Object Detection Leveraging Frame Difference and Morphological Filtering 2

FPGA-based Moving Object Detection Leveraging Frame Differen...

引用

2nd International conference on Computer Vision and Intelligent Technology

作者： Wang, Yuexin Jing, Xiaochen Wang, Wenhao Zhu, Mengli Zhu, Dongchen Chinese Acad Sci Shanghai Inst Microsyst & Informat Technol Bion Vis Syst Lab Shanghai Peoples R China Univ Chinese Acad Sci Beijing Peoples R China

ISBN: (纸本)9798331540050;9798331540043

Moving object detection plays a significant role in video surveillance. However, existing moving object detection methods often rely on software implementations, which means low real-time performance and high power consumption. This paper's core detection algorithm employs a frame difference method that is enhanced by morphological filtering. Additionally, we propose an architecture that integrates FPGA(Field Programmable Gate Array) and ARM(Advanced RISC Machine), fully leveraging the parallel computing advantages of FPGA and the high processing efficiency of ARM. The system utilizes a ZYNQ7000 SoC, coupled with an OV7725 camera for image capture and DDR3 SDRAM for data caching, to address the challenges of high-speed data processing and low power consumption. Experimental results show that the system meets the requirements for high real-time performance and low power consumption with a frame rate of 85.9375 frames per second and a total power consumption of 1.101 W.

关键词： FPGA frame difference method moving object detection real-time video processing

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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

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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 ...

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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

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