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Contactless Respiratory Rate Monitoring For ICU Patients Based On Unsupervised Learning

Contactless Respiratory Rate Monitoring For ICU Patients Bas...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Zimeng Liu Bin Huang Chun-Liang Lin Chieh-Liang Wu Changchen Zhao Wen-Cheng Chao Yu-Cheng Wu Yadan Zheng Zhiru Wang Hangzhou Innovataion Institute Beihang University School of Automation Science and Electrical Engineering Beihang University College of Electrical Engineering and Computer Science National Chung Hsing University Department of Critical Care Medicine Taichung Veterans General Hospital Department of Post-Baccalaureate Medicine College of Medicine National Chung Hsing University Big Data Center National Chung Hsing University Computer Science School Beijing University of Posts and Telecommunications Graduate School of Translation and Interpretation Beijing Foreign Studies University

Recently, the task of contactless physiological signal monitoring based on deep learning technologies has attracted a large number of scholars. However, few studies focus on the application of real-world scenarios, especially in clinical medicine scenes. In this paper, a novel video-based contactless respiratory rate measurement algorithm is developed for the Intensive Care Unit (ICU) patients. Firstly, a large-scale clinical real-world database towards ICU patient is collected in this study. Then, based on the dataset, the unsupervised learning is first introduced to extract the respiration waveform from the chest area of patients. Lastly, a respiratory rate estimator based on neural networks is proposed and trained on a periodical physiological signal simulation dataset, and utilizes the transfer learning technique to extract the respiratory rate from only 10-second respiration waveform. We obtained estimated respiratory rate with an MAE of 2.8 breaths/min and an STD of 3.0 breaths/min against the reference value computed from the specialized medical device. Extensive experiments demonstrate that our proposed methods achieve competitive results over state-of-the-art (SOTA) method in the real-world scenario.

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Video Analytics for Detecting Motorcyclist Helmet Rule Violations

Video Analytics for Detecting Motorcyclist Helmet Rule Viola...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Chun-Ming Tsai Jun-Wei Hsieh Ming-Ching Chang Guan-Lin He Ping-Yang Chen Wei-Tsung Chang Yi-Kuan Hsieh Department of Computer Science University of Taipei Taipei Taiwan College of AI and Green Energy National Yang Ming Chiao Tung University Tainan Taiwan Department of Computer Science University at Albany State University of New York NY USA

The use of helmets is essential for motorcyclists' safety, but non-compliance with helmet rules remains a common issue. In this study, we extend the frontier of AI video analytic technologies for detecting violations of helmet rules among motorcyclists. Our method can handle highly challenging conditions for traditional methods, including occlusions, fast vehicle movement, shadows, large viewing angles, poor illumination and weather conditions. We adopt the widely used YOLOv7 object detector and develop a first baseline using YOLOv7-E6E. We further develop two improved versions, namely YOLOv7-CBAM and YOLOv7-SimAM that better address the challenges. Experiments are performed on the 2023 AI City Challenge Track 5 contest benchmark. Evaluation on the 100 test videos of the contest demonstrates the effectiveness of our approach. The baseline YOLOv7-E6E model trained with image size 1920 achieves 0.6112 mAP. The YOLOv7-CBAM achieves 0.6389 mAP, and YOLOv7-SimAM achieves 0.6422 mAP, where both are trained with image size 1280. These models rank sixth, fifth, and fourth on the public leaderboard, respectively, which outperforms over 36 global participating teams. The code for our models is available at: https://***/cmtsai2023/AICITY2023_Track5_DVHRM.

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Clothed Human Performance Capture with a Double-layer Neural Radiance Fields

Clothed Human Performance Capture with a Double-layer Neural...

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2023 ieee/CVF conference on computer vision and pattern recognition, CVPR 2023

作者： Wang, Kangkan Zhang, Guofeng Cong, Suxu Yang, Jian Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education China Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology China State Key Laboratory of CAD&CG Zhejiang University China

This paper addresses the challenge of capturing performance for the clothed humans from sparse-view or monocular videos. Previous methods capture the performance of full humans with a personalized template or recover the garments from a single frame with static human poses. However, it is inconvenient to extract cloth semantics and capture clothing motion with one-piece template, while single frame-based methods may suffer from instable tracking across videos. To address these problems, we propose a novel method for human performance capture by tracking clothing and human body motion separately with a double-layer neural radiance fields (NeRFs). Specifically, we propose a double-layer NeRFs for the body and garments, and track the densely deforming template of the clothing and body by jointly optimizing the deformation fields and the canonical double-layer NeRFs. In the optimization, we introduce a physics-aware cloth simulation network which can help generate physically plausible cloth dynamics and body-cloth interactions. Compared with existing methods, our method is fully differentiable and can capture both the body and clothing motion robustly from dynamic videos. Also, our method represents the clothing with an independent NeRFs, allowing us to model implicit fields of general clothes feasibly. The experimental evaluations validate its effectiveness on real multi-view or monocular videos. ©2023 ieee.

关键词： Multilayer neural networks

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Analyzing Participants’ Engagement during Online Meetings Using Unsupervised Remote Photoplethysmography with Behavioral Features

Analyzing Participants’ Engagement during Online Meetings U...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Alexander Vedernikov Zhaodong Sun Virpi-Liisa Kykyri Mikko Pohjola Miriam Nokia Xiaobai Li Center for Machine Vision and Signal Analysis University of Oulu Finland Department of Psychology University of Jyväskylä Finland State Key Laboratory of Blockchain and Data Security Zhejiang University China

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Engagement measurement finds application in healthcare, education, services. The use of physiological and behavioral features is viable, but the impracticality of traditional physiological measurement arises due to the need for contact sensors. We demonstrate the feasibility of unsupervised remote photoplethysmography (rPPG) as an alternative for contact sensors in deriving heart rate variability (HRV) features, then fusing these with behavioral features to measure engagement in online group meetings. Firstly, a unique Engagement Dataset of online interactions among social workers is collected with granular engagement labels, offering insight into virtual meeting dynamics. Secondly, a pre-trained rPPG model is customized to reconstruct rPPG signals from video meetings in an unsupervised manner, enabling the calculation of HRV features. Thirdly, the feasibility of estimating engagement from HRV features using short observation windows, with a notable enhancement when using longer observation windows of two to four minutes, is demonstrated. Fourthly, the effectiveness of behavioral cues is evaluated when fused with physiological data, which further enhances engagement estimation performance. An accuracy of 94% is achieved when only HRV features are used, eliminating the need for contact sensors or ground truth signals; use of behavioral cues raises the accuracy to 96%. Facial analysis offers precise engagement measurement, beneficial for future applications.

关键词： Accuracy Atmospheric measurements Estimation Web conferencing Sensor phenomena and characterization Photoplethysmography Particle measurements

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NTIRE 2024 Challenge on Bracketing Image Restoration and Enhancement: Datasets, Methods and Results

NTIRE 2024 Challenge on Bracketing Image Restoration and Enh...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Zhilu Zhang Shuohao Zhang Renlong Wu Wangmeng Zuo Radu Timofte Xiaoxia Xing Hyunhee Park Sejun Song Changho Kim Xiangyu Kong Jinlong Wu Jianxing Zhang Jingfan Tan Zikun Liu Wenhan Luo Wenjie Lin Chengzhi Jiang Mingyan Han Zhen Liu Ting Jiang Jinting Luo Shen Cheng Linze Li Xinhan Niu Shuaicheng Liu Kexin Dai Kangzhen Yang Tao Hu Xiangyu Chen Yu Cao Qingsen Yan Yanning Zhang Genggeng Chen Yongqing Yang Wei Dong Xinwei Dai Yuanbo Zhou Xintao Qiu Hui Tang Wei Deng Qingquan Gao Tong Tong Peng Zhang Yifei Chen Wenbo Xiong Zhijun Song Pu Cheng Taolue Feng Yunqing He Daiguo Zhou Ying Huang Xiaowen Ma Peng Wu

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Low-light photography presents significant challenges. Multi-image processing methods have made numerous attempts to obtain high-quality photos, yet remain unsatisfactory. Recently, bracketing image restoration and enhancement has received increased attention. By leveraging the full potential of multi-exposure images, several tasks (including denoising, deblurring, high dynamic range enhancement, and super-resolution) can be jointly addressed. This paper reviews the NTIRE 2024 challenge on bracketing image restoration and enhancement. In the challenge, participants are required to process multi-exposure RAW images to generate noise-free, blur-free, high dynamic range, and even higher-resolution RAW images. The challenge comprises two tracks. Track 1 does not incorporate the super-resolution task, whereas Track 2 does. Each track featured five teams participating in the final testing phase. The proposed methods establish new state-of-the-art performance benchmarks.

关键词： Photography computer vision Reviews conferences Superresolution Noise reduction Benchmark testing

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NTIRE 2024 Restore Any Image Model (RAIM) in the Wild Challenge

NTIRE 2024 Restore Any Image Model (RAIM) in the Wild Challe...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Jie Liang Radu Timofte Qiaosi Yi Shuaizheng Liu Lingchen Sun Rongyuan Wu Xindong Zhang Hui Zeng Lei Zhang Yibin Huang Shuai Liu Yongqiang Li Chaoyu Feng Xiaotao Wang Lei Lei Yuxiang Chen Xiangyu Chen Qiubo Chen Fengyu Sun Mengying Cui Jiaxu Chen Zhenyu Hu Jingyun Liu Wenzhuo Ma Ce Wang Hanyou Zheng Wanjie Sun Zhenzhong Chen Ziwei Luo Fredrik K. Gustafsson Zheng Zhao Jens Sjölund Thomas B. Schön Xiong Dun Pengzhou Ji Yujie Xing Xuquan Wang Zhanshan Wang Xinbin Cheng Jun Xiao Chenhang He Xiuyuan Wang Zhi-Song Liu Zimeng Miao Zhicun Yin Ming Liu Wangmeng Zuo Shuai Li

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In this paper, we review the NTIRE 2024 challenge on Restore Any Image Model (RAIM) in the Wild. The RAIM challenge constructed a benchmark for image restoration in the wild, including real-world images with/without reference ground truth in various scenarios from real applications. The participants were required to restore the real-captured images from complex and unknown degradation, where generative perceptual quality and fidelity are desired in the restoration result. The challenge consisted of two tasks. Task one employed real referenced data pairs, where quantitative evaluation is available. Task two used unpaired images, and a comprehensive user study was conducted. The challenge attracted more than 200 registrations, where 39 of them submitted results with more than 400 submissions. Top-ranked methods improved the state-of-the-art restoration performance and obtained unanimous recognition from all 18 judges. The proposed datasets are available at https : //***/file/d/1DqbxUoiUqkAIkExu3jZAqoElr_nu1IXb/view?usp=sharing and the homepage of this challenge is at https : //***/competitions/17632.

关键词： Degradation computer vision Reviews conferences Computational modeling Benchmark testing Image restoration

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NTIRE 2021 Challenge on Quality Enhancement of Compressed Video: Methods and Results

NTIRE 2021 Challenge on Quality Enhancement of Compressed Vi...

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ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Yang, Ren Timofte, Radu Liu, Jing Xu, Yi Zhang, Xinjian Zhao, Minyi Zhou, Shuigeng Chan, Kelvin C. K. Zhou, Shangchen Xu, Xiangyu Loy, Chen Change Li, Xin Liu, Fanglong Zheng, He Jiang, Lielin Zhang, Qi He, Dongliang Li, Fu Dang, Qingqing Huang, Yibin Maggioni, Matteo Fu, Zhongqian Xiao, Shuai Li, Cheng Tanay, Thomas Song, Fenglong Chao, Wentao Guo, Qiang Liu, Yan Li, Jiang Qu, Xiaochao Hou, Dewang Yang, Jiayu Jiang, Lyn You, Di Zhang, Zhenyu Mou, Chong Koshelev, Iaroslav Ostyakov, Pavel Somov, Andrey Hao, Jia Zou, Xueyi Zhao, Shijie Sun, Xiaopeng Liao, Yiting Zhang, Yuanzhi Wang, Qing Zhan, Gen Guo, Mengxi Li, Junlin Lu, Ming Ma, Zhan Michelini, Pablo Navarrete Wang, Hai Chen, Yiyun Guo, Jingyu Zhang, Liliang Yang, Wenming Kim, Sijung Oh, Syehoon Wang, Yucong Cai, Minjie Hao, Wei Shi, Kangdi Li, Liangyan Chen, Jun Gao, Wei Liu, Wang Zhang, Xiaoyu Zhou, Linjie Lin, Sixin Wang, Ru Swiss Fed Inst Technol Comp Vis Lab Zurich Switzerland Bilibili Inc Shanghai Peoples R China Fudan Univ Shanghai Peoples R China Nanyang Technol Univ S Lab Singapore Singapore Baidu Inc Dept Comp Vis Technol VIS Beijing Peoples R China Huawei Technol Co Ltd Huawei Noahs Ark Lab Shenzhen Peoples R China Meitu Inc MTLab Beijing Peoples R China Peking Univ Shenzhen Peoples R China Tencent Shenzhen Peoples R China Skolkovo Inst Sci & Technol Moscow Russia Huawei Noahs Ark Lab Shenzhen Peoples R China HiSilicon Shanghai Technol CO LTD Shanghai Peoples R China ByteDance Ltd Shenzhen Peoples R China Nanjing Univ Sch Elect Sci & Engn Nanjing Peoples R China BOE Technol Grp Co Ltd Beijing Peoples R China Tsinghua Univ Shenzhen Peoples R China SZ Da Jiang Innovat Sci & Technol Co Ltd Shenzhen Peoples R China Bluedot Seoul South Korea Hunan Univ Coll Comp Sci & Elect Engn Changsha Peoples R China McMaster Univ Hamilton ON Canada Peking Univ Sch Elect & Comp Engn Shenzhen Grad Sch Shenzhen Peoples R China Peng Cheng Lab Shenzhen Peoples R China

ISBN: (纸本)9781665448994

This paper reviews the first NTIRE challenge on quality enhancement of compressed video, with focus on proposed solutions and results. In this challenge, the new Large-scale Diverse Video (LDV) dataset is employed. The challenge has three tracks. Tracks 1 and 2 aim at enhancing the videos compressed by HEVC at a fixed QP, while Track 3 is designed for enhancing the videos compressed by x265 at a fixed bit-rate. Besides, the quality enhancement of Tracks 1 and 3 targets at improving the fidelity (PSNR), and Track 2 targets at enhancing the perceptual quality. The three tracks totally attract 482 registrations. In the test phase, 12 teams, 8 teams and 11 teams submitted the final results of Tracks 1, 2 and 3, respectively. The proposed methods and solutions gauge the state-of-the-art of video quality enhancement. The homepage of the challenge: https://***/RenYang-home/NTIRE21_VEnh

关键词： computer vision Target tracking conferences Quality assessment pattern recognition Video recording

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ART•V: Auto-Regressive Text-to-Video Generation with Diffusion Models

ART•V: Auto-Regressive Text-to-Video Generation with Diffus...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Wenming Weng Ruoyu Feng Yanhui Wang Qi Dai Chunyu Wang Dacheng Yin Zhiyuan Zhao Kai Qiu Jianmin Bao Yuhui Yuan Chong Luo Yueyi Zhang Zhiwei Xiong University of Science and Technology of China Microsoft Research Asia

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

We present ART•V, an efficient framework for autoregressive video generation with diffusion models. Unlike existing methods that generate entire videos in one-shot, ART•V generates a single frame at a time, conditioned on the previous ones. The framework offers three distinct advantages. First, it only learns simple continual motions between adjacent frames, therefore avoiding modeling complex long-range motions that require huge training data. Second, it preserves the high-fidelity generation ability of the pre-trained image diffusion models by making only minimal network modifications. Third, it can generate arbitrarily long videos conditioned on a variety of prompts such as text, image or their combinations, making it highly versatile and flexible. To combat the common drifting issue in AR models, we propose masked diffusion model which implicitly learns which information can be drawn from reference images rather than network predictions, in order to reduce the risk of generating inconsistent appearances that cause drifting. Moreover, we further enhance generation coherence by conditioning it on the initial frame, which typically contains minimal noise. This is particularly useful for long video generation. When trained for only two weeks on four GPUs, ART•V already can generate videos with natural motions, rich details and a high level of aesthetic quality. Besides, it enables various appealing applications, e.g. composing a long video from multiple text prompts.

关键词： computer vision conferences Noise Training data Coherence Predictive models Diffusion models

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PRB-FPN+: Video Analytics for Enforcing Motorcycle Helmet Laws

PRB-FPN+: Video Analytics for Enforcing Motorcycle Helmet La...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Bor-Shiun Wang Ping-Yang Chen Yi-Kuan Hsieh Jun-Wei Hsieh Ming-Ching Chang JiaXin He Shin-You Teng HaoYuan Yue Yu-Chee Tseng Department of Computer Science National Yang Ming Chiao Tung University Hsinchu Taiwan College of AI and Green Energy National Yang Ming Chiao Tung University Tainan Taiwan Department of Computer Science University at Albany State University of New York NY USA Department of Computer Science Vanderbilt University USA Department of Computer Science The Chinese University of Hong Kong China

We present a video analytic system for enforcing motorcycle helmet regulation as a participation to the AI City Challenge 2023 [18] Track 5 contest. The advert of powerful object detectors enables real-time localization of the road users and even the ability to determine if a motorcyclist or a rider is wearing a helmet. Ensuring road safety is important, as the helmets can effectively provide protection against severe injuries and fatalities. However, monitoring and enforcing helmet compliance is challenging, given the large number of motorcyclists and limited visual input such as occlusions. To address these challenges, we propose a novel two-step approach. First, we introduce the PRB-FPN+, a state-of-the-art detector that excels in object localization. We also explore the benefits of deep supervision by incorporating auxiliary heads within the network, leading to enhanced performance of our deep learning architectures. Second, we utilize an advanced tracker named SMILEtrack to associate and refine the target track-lets. Comprehensive experimental results demonstrate that the PRB-FPN+ outperforms the state-of-the-art detectors on MS-COCO. Our system achieved a remarkable rank of 8 on the AI City Challenge 2023 [18] Track 5 Public Leader-board. Code implementation is available at: https://***/NYCU-AICVLab/AICITY_2023_Track5.

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SUNDIAL: 3D Satellite Understanding through Direct, Ambient, and Complex Lighting Decomposition

SUNDIAL: 3D Satellite Understanding through Direct, Ambient,...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Nikhil Behari Akshat Dave Kushagra Tiwary William Yang Ramesh Raskar Harvard University Massachusetts Institute of Technology

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Estimating 3D geometry of terrain from satellite imagery is essential for environmental science, urban planning, agriculture, and disaster response. However, traditional 3D modeling techniques face unique challenges in the remote sensing context, including limited multi-view baselines over extensive regions, varying direct, ambient, and complex illumination conditions, and time-varying scene changes across captures. In this work, we introduce SUNDIAL, a comprehensive approach to 3D reconstruction of satellite imagery using neural radiance fields. We jointly learn satellite scene geometry, illumination components, and sun direction in this single-model approach, and propose a secondary shadow ray casting technique to 1) improve scene geometry using oblique sun angles to render shadows, 2) enable physically-based disentanglement of scene albedo and illumination, and 3) determine the components of illumination from direct, ambient (sky), and complex sources. To achieve this, we incorporate lighting cues and geometric priors from remote sensing literature in a neural rendering approach, modeling physical properties of satellite scenes such as shadows, scattered sky illumination, and complex illumination of vegetation and water. We evaluate the performance of SUNDIAL against existing NeRF-based techniques for satellite scene modeling and demonstrate improved scene and lighting disentanglement, novel view and lighting rendering, and geometry and sun direction estimation on challenging scenes with small baselines, sparse inputs, and variable illumination.

关键词： Geometry Casting Satellites Three-dimensional displays Lighting Rendering (computer graphics) Satellite images

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