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

共 8693 条记录，以下是181-190 订阅

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PosEmo - An automated system for measuring user interest and attitude in real time 57

PosEmo - An automated system for measuring user interest and...

引用

57th Hawaii International conference on System Sciences (HICSS)

作者： Buchwald, Mikolaj Nowak, Jan Kupinski, Szymon Biadala, Magdalena Behnke, Maciej Polish Acad Sci Poznan Supercomp & Networking Ctr Network Serv Dept Poznan Poland Adam Mickiewicz Univ Dept Psychol & Cognit Sci Poznan Poland

ISBN: (纸本)9780998133171

Given the increasing prevalence of digital services across various aspects of life, it has become crucial to understand and recognize the mental states of individuals interacting with artificial systems. To address this concern, we aimed to develop the PosEmo - an automated application that can assess individuals' affective states using a video web camera. While studying affective states, we focused on two kinds of emotional behavior: approach/avoidance behavior and behavioral freezing/activation. To measure these behaviors, we use computer vision techniques to track the movement of the participant's head in video recordings, as well as in real-time video streaming. This method offered the seated research participant convenience, replicability, and non-intrusiveness. Drawing from established theoretical frameworks and supported by initial empirical findings, we developed the software and validated it in the online experiment. We found that PosEmo recognized whether people watched negative, neutral, or positive videos. Thus, our innovative approach enables us to accurately estimate people's affective states. In sum, by adopting a human-centered approach, we combined artificial intelligence methodologies to create an innovative system supporting human-computer interaction. Our system's potential research applications span various domains, such as psychology, cognitive science, usability studies, psychotherapy sessions, content quality assessment, and education.

关键词： image processing artificial intelligence affective computing psychology emotions

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Object Re-Identification from Point Clouds

Object Re-Identification from Point Clouds

引用

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

作者： Therien, Benjamin Huang, Chengjie Chow, Adrian Czarnecki, Krzysztof Univ Waterloo Waterloo ON Canada

ISBN: (纸本)9798350318920;9798350318937

Object re-identification (ReID) from images plays a critical role in application domains of image retrieval (surveillance, retail analytics, etc.) and multi-object tracking (autonomous driving, robotics, etc.). However, systems that additionally or exclusively perceive the world from depth sensors are becoming more commonplace without any corresponding methods for object ReID. In this work, we fill the gap by providing the first large-scale study of object ReID from point clouds and establishing its performance relative to image ReID. To enable such a study, we create two large-scale ReID datasets with paired image and LiDAR observations and propose a lightweight matching head that can be concatenated to any set or sequence processing backbone (e.g., PointNet or ViT), creating a family of comparable object ReID networks for both modalities. Run in Siamese style, our proposed point cloud ReID networks can make thousands of pairwise comparisons in real-time (10 Hz). Our findings demonstrate that their performance increases with higher sensor resolution and approaches that of image ReID when observations are sufficiently dense. Our strongest network trained at the largest scale achieves ReID accuracy exceeding 90% for rigid objects and 85% for deformable objects (without any explicit skeleton normalization). To our knowledge, we are the first to study object re-identification from real point cloud observations. Our code is available at https://***/bentherien/point-cloud-reid.

关键词： 3D computer vision Algorithms Algorithms Applications Robotics video recognition and understanding

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Hardware Implementation of Low-latency image Denoising with Wavelet Shrinkage and Noise2Noise-based Learning for Ultra-high Definition Cameras

Hardware Implementation of Low-latency Image Denoising with ...

引用

2024 IEEE International conference on Consumer Electronics, ICCE 2024

作者： Kikuchi, Kodai Tomioka, Kohei Honji, Akira Usui, Takenobu Hirakawa, Keigo Kitamura, Kazuya Science and Technology Research Laboratories Japan Broadcasting Corporation Tokyo Japan University of Dayton Department of Electrical and Computer Engineering OH United States

ISBN: (纸本)9798350324136

There is a growing demand for real-time image denoising in low-light shooting with ultra-high definition cameras. This paper presents a denoising method that incorporates Haar-wavelet shrinkage denoising and a minimum mean square error estimation based on an extension of the Noise2Noise learning technique, which can perform low-latency processing and can be adapted to non-linear processed signals without prior knowledge of noise and signal distributions. By introducing a two-dimensional kernel to reduce memory usage in the coring function estimator, we demonstrate real-time hardware denoising for luminance signals of 4K 60 fps video with a delay of less than one millisecond. © 2024 IEEE.

关键词： FPGA Haar-wavelet image denoising MMSE estimation Noise2Noise UHD

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video SAR Moving Target Detection System Based on FPGA 8

Video SAR Moving Target Detection System Based on FPGA

引用

8th International conference on Signal and image processing, ICSIP 2023

作者： Tong, Wenhao Wei, Boxu Yu, Anxi Dong, Zhen He, Zhihua Tang, Feixiang College of Electronic Science and Technology Changsha410073 China

ISBN: (纸本)9798350397932

video synthetic aperture radar (video SAR) has become a research hotspot in the SAR field due to its characteristic of continuous monitoring. Compared to traditional SAR, video SAR provides the ability to dynamically monitor ground targets. The defocusing and displacement generated during the target motion process result in the radar only capturing occlusion shadows, making shadow detection a new method for motion target recognition in video SAR. In this paper, we propose a multi-frame fusion convolutional neural network (CNN) based on the problem of low detection rate due to shadows being too close to the environmental background. We fully utilize the dynamic information of the video through methods such as video stabilization, histogram equalization, and multi-frame image fusion, effectively improving the detection rate. In addition, we reduce the volume of the neural network through network quantization and calibration methods and successfully deploy it in field-programmable gate arrays (FPGA), achieving low-power, small-volume real-time motion target detection. © 2023 IEEE.

关键词： Deep learning

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Drifting resistant algorithm for video target tracking based on Kernelized Correlation Filters framework

Drifting resistant algorithm for video target tracking based...

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2024 International conference on Automation and Intelligent Technology, ICAIT 2024

作者： Shan, Zili Zhang, Xuan Zhai, Pengfei Liang, Shuo Chen, Jinyong The 54th Research Institute of China Electronics Technology Group Corporation Shijiazhuang China Hebei Open University Shijiazhuang China

ISBN: (数字)9781510685819

ISBN: (纸本)9781510685802

video target tracking has a wide range of application value in the field of automatic driving, UAV target tracking, security monitoring, etc. How to maintain stable tracking of the target among video data frames is the focus of the research. A robust tracking algorithm that effectively solves the target drift problem is proposed for the problem of target loss due to image perturbation, scale change, target occlusion and other disturbances when the KCF algorithm is used for video target tracking. The algorithm is based on the KCF algorithm framework, which proposes a multi-scale sampling strategy and designs a multiple classifier screening algorithm to ensure the accuracy of the target template. Through experimental verification, the algorithm can effectively solve the drift problem in the tracking process and realize the continuous accurate tracking of the target. The algorithm provides a real-world application reference for engineering applications of real-time video data processing. © The Authors.

关键词： Target tracking

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Application of artificial intelligence algorithm model in fault diagnosis of interactive information 6

Application of artificial intelligence algorithm model in fa...

引用

6th International conference on image, video processing, and Artificial Intelligence, IVPAI 2024

作者： Jin, Hulin Jin, Zhiran Kim, Yong-Guk International Institute of Engineering Psychology DenverCO80202 United States Foothill Preparatory School Temple CityCA91780 United States Department of Computer Engineering Sejong University Seoul05006 Korea Republic of

ISBN: (纸本)9781510681781

Interactive information fault diagnosis technology is a new type of fault diagnosis technology which is integrated by information fusion, artificial intelligence, computer science and other disciplines. It can extract interactive information data of equipment in real time, analyze the characteristics of fault information, and then find the change and trend of equipment operating status. However, there are still some shortcomings in the practical application of this technology, for example, it is difficult to deal with a large number of complex interactive information data, and it is difficult to carry out effective fault diagnosis. Therefore, an intelligent fault diagnosis method with improved grey relational degree is proposed in this paper. This method can effectively extract the interactive information feature data of the equipment, and carry out the correlation analysis of the fault feature data through the grey correlation analysis algorithm, which provides a new idea for the fault diagnosis of the equipment. © 2024 SPIE.

关键词： Correlation coefficients Evolutionary algorithms Matrices Artificial intelligence Fuzzy logic Algorithms Data processing Algorithm development Feature extraction Intelligence systems

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Zero-shot video Change Detection for real-life Industrial Applications 16

Zero-shot Video Change Detection for Real-life Industrial Ap...

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16th Annual conference of the Prognostics and Health Management Society, PHM 2024

作者： Alam, Mahbubul Zhuge, Huimin Gonzalez, Teresa Farahat, Ahmed Wang, Song Gupta, Chetan Industrial AI Lab Hitachi America Ltd. Santa ClaraCA95054 United States

ISBN: (纸本)9781936263059

Change detection is crucial for various industrial applications. Although image change detection datasets are abundant, the collection of labeled video data is time-consuming, expensive, and cumbersome. This scarcity of labeled data motivates the development of few-shot or zero-shot video change detection techniques which may generalize well to new situations. Existing video change detection methods require large amounts of labeled data, are task-specific, and difficult to generalize. Therefore, in this paper, we propose a zero-shot video change detection algorithm using pre-trained deep learning models and conventional image processing techniques. Our approach identifies matching frames from input videos, adjusts lighting conditions if necessary, and uses an existing object detection model to identify objects in both frames. The method is easily generalizable by making few changes. We evaluate our proposed method on the VDAO dataset collected in a cluttered industrial environment and demonstrate its effectiveness in detecting changes between pairs of videos containing single and multiple objects. © 2024 Prognostics and Health Management Society. All rights reserved.

关键词： Zero-shot learning

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Deep neural network based video air cloud detection algorithm implementation research 4

Deep neural network based video air cloud detection algorith...

引用

4th International conference on Advanced Algorithms and Signal image processing, AASIP 2024

作者： Guo, Lijuan Li, Lu Xie, Guoshan Song, Guanjun Pang, Hai Cui, Peiyi Electric Power Research Institute of Guangxi Power Grid Co. Ltd. Guangxi Nanning530023 China

ISBN: (数字)9781510682818

ISBN: (纸本)9781510682801

In order to achieve the possibility and probability of discovering the violation of operation tasks through previous data, this paper proposes an intelligent identification algorithm for safety risk of transmission line maintenance operations based on deep learning. Firstly, a large amount of transmission line maintenance operation image data is collected, secondly, the size of image chunks is determined by using a priori information and experiments to reduce the number of layers of convolution and reduce the computing time. Finally, a standard library of image samples of transmission lines is successfully constructed, which achieves the standardised collection of inspection defect samples with key features of multi-source data and the standardised management of transmission equipment information, unified labelling transformation and efficient storage and retrieval of defect information. The experimental results show that real-time monitoring and early warning of potential safety risks in transmission line maintenance operations can be achieved through the deep learning-based intelligent identification algorithm for safety risks in transmission line maintenance operations, thus improving the safety and efficiency of maintenance operations. © 2024 SPIE.

关键词： Information management

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Computer Vision Based Hybrid Classroom Attention Monitoring

Computer Vision Based Hybrid Classroom Attention Monitoring

引用

2024 IEEE International conference on Information Technology, Electronics and Intelligent Communication Systems, ICITEICS 2024

作者： Rawat, Saniya Rodrigues, Malivia Sheregar, Prateeksha Wagaskar, Kalpita Ajinkya Tripathy, Amiya Kumar Mumbai India

ISBN: (纸本)9798350382693

This research presents a novel computer vision-based attention monitoring system designed for both online and offline contexts. Leveraging advanced image processing and machine learning algorithms, the system analyzes human gaze patterns, eye movements, and facial expressions to accurately gauge attention levels. In online scenarios, the system employs real-time webcam-based gaze tracking and facial recognition to provide immediate insights into user engagement during activities like video conferencing and virtual meetings. For offline analysis, recorded video footage is retrospectively examined, facilitating applications in education, workplace productivity, and user experience assessments. Privacy considerations are addressed through the implementation of privacy-preserving techniques. Experimental results demonstrate the system's efficacy in monitoring attention dynamics across diverse settings, contributing to a deeper understanding of human attention in various domains. © 2024 IEEE.

关键词： video conferencing

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video-ANALYTICS TASK-AWARE QUAD-TREE PARTITIONING AND QUANTIZATION FOR HEVC 29

VIDEO-ANALYTICS TASK-AWARE QUAD-TREE PARTITIONING AND QUANTI...

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IEEE International conference on image processing (ICIP)

作者： Singh, Praneet Delp, Edward J. Reibman, Amy R. Purdue Univ Elmore Sch Elect & Comp Engn W Lafayette IN USA

ISBN: (数字)9781665496209

ISBN: (纸本)9781665496209

video analytics systems designed for computer vision tasks use deep learning models that rely on high-quality input data to maximize performance. However, in a real-world system, these inputs are often compressed using video codecs such as HEVC. video compression degrades the quality of the inputs, thereby degrading the performance of these models. Region-of-interest (ROI) coding enables bits to be allocated to improve performance;however, the method to select regions should be computationally simple since it must occur during or before the video is compressed and transmitted for further processing. In this paper, we propose a task-aware quad-tree (TA-QT) partitioning and quantization method to achieve ROI coding for HEVC and other video coding standards. TAQT uses a lightweight edge-based model to guide task-aware video encoding to improve end-stage video analytics (ESVA) performance while reducing both bit-rate and encoding time. We demonstrate the effectiveness of our approach in terms of (a) the performance of the ESVA on compressed inputs, (b) transmission bit-rates, and (c) encoding time.

关键词： video analytics computer vision deep learning HEVC HM video compression task-aware

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