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Multi-camera People Tracking With Mixture of Realistic and Synthetic Knowledge

Multi-camera People Tracking With Mixture of Realistic and S...

引用

2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Nguyen, Quang Qui-Vinh Le, Huy Dinh-Anh Chau, Truc Thi-Thanh Luu, Duc Trung Chung, Nhat Minh Ha, Synh Viet-Uyen International University School of Computer Science and Engineering Ho Chi Minh City Viet Nam Vietnam National University Ho Chi Minh City Viet Nam

ISBN: (纸本)9798350302493

This paper presents a solution for Track 1 of the AI City Challenge 2023, which involves Multi-Camera People Tracking in indoor scenarios. The proposed framework comprises four modules: Vehicle detection, ReID feature extraction, single-camera multi-target tracking (SCMT), single-camera matching, and multi-camera matching. A significant contribution of our approach is the introduction of ID switch detection and ID switch splitting using the Gaussian mixture model, which efficiently addresses the problem of tracklets with ID switches. Furthermore, our system performs well in matching both synthetic and real data. The proposed R-matching algorithm performs exceptionally well in real scenarios despite being trained on synthetic data. Experimental results on the public test set of 2023 AI City Challenge Track 1 demonstrate the efficacy of the proposed approach, achieving an IDF1 of 94.17% and securing 2nd position on the leaderboard. Codes will be available at https://***/nguyenquivinhquang/Multi-camera-People-Tracking-With-Mixture-of-Realistic-and-Synthetic-Knowledge © 2023 ieee.

关键词： Feature extraction

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3D-IntPhys: Towards More Generalized 3D-grounded Visual Intuitive Physics under Challenging Scenes

3D-IntPhys: Towards More Generalized 3D-grounded Visual Intu...

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2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Xue, Haotian Torralba, Antonio Tenenbaum, Joshua Yamins, Daniel Li, Yunzhu Tung, Hsiao-Yu Georgia Tech United States Mit United States Stanford Univeristy United States

ISBN: (纸本)9798350302493

Given a visual scene, humans have strong intuitions about how a scene can evolve over time under given actions. The intuition, often termed visual intuitive physics, is a critical ability that allows us to make effective plans to manipulate the scene to achieve desired outcomes without relying on extensive trial and error. In this paper, we present a framework capable of learning 3D-grounded visual intuitive physics models from videos of complex scenes with fluids. Our method is composed of a conditional Neural Radiance Field (NeRF)-style visual frontend and a 3D point-based dynamics prediction backend, using which we can impose strong relational and structural inductive bias to capture the structure of the underlying environment. Unlike existing intuitive point-based dynamics works that rely on the supervision of dense point trajectory from simulators, we relax the requirements and only assume access to multi-view RGB images and (imperfect) instance masks acquired using color prior. This enables the proposed model to handle scenarios where accurate point estimation and tracking are hard or impossible. We generate datasets including three challenging scenarios involving fluid, granular materials, and rigid objects in the simulation. The datasets do not include any dense particle information so most previous 3D-based intuitive physics pipelines can barely deal with that. We show our model can make long-horizon future predictions by learning from raw images and significantly outperforms models that do not employ an explicit 3D representation space. We also show that once trained, our model can achieve strong generalization in complex scenarios under extrapolate settings. © 2023 ieee.

关键词： computer vision

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NTIRE 2023 Video Colorization Challenge

NTIRE 2023 Video Colorization Challenge

引用

2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Kang, Xiaoyang Lin, Xianhui Zhang, Kai Hui, Zheng Xiang, Wangmeng He, Jun-Yan Li, Xiaoming Ren, Peiran Xie, Xuansong Timofte, Radu Pan, Jinshan Peng, Zhongzheng Zhang, Qiyan Dong, Jiangxin Tang, Jinhui Li, Jinjing Lin, Chichen Li, Qipei Liang, Qirong Gang, Ruipeng Liu, Xiaofeng Feng, Shuang Liu, Shuai Wang, Hao Feng, Chaoyu Bai, Furui Zhang, Yuqian Shao, Guangqi Wang, Xiaotao Lei, Lei Chen, Siqi Zhang, Yu Xu, Hanning Liu, Zheyuan Zhang, Zhao Luo, Yan Zuo, Zhichao Damo Academy Alibaba Group China Computer Vision Lab Eth Zürich Switzerland Nanyang Technological University Singapore University of Würzburg Germany Nanjing University of Science and Technology China Communication University of China Beijing100024 China Academy of Broadcasting Science Nrta Beijing100866 China Uhdtv Research and Application Laboratory Beijing100176 China Xiaomi Inc. China School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing China Zjut China China

ISBN: (纸本)9798350302493

This paper reviews the video colorization challenge on the New Trends in Image Restoration and Enhancement (NTIRE) workshop, held in conjunction with CVPR 2023. The target of this challenge is converting grayscale videos into color videos with better colorization performance and temporal consistency. The challenge consists of two tracks. For Track 1, the goal is achieving the best FID (Fréchet Inception Distance) while being constrained to maintain or improve over the baseline method in terms of the temporal-consistency metric. The Color Distribution Consistency (CDC) index is used as the temporal consistency evaluation metric in this challenge. For Track 2, the target is to obtain a solution with the best CDC result while being constrained to maintain or improve over the baseline method in terms of FID. We use DeOldify-video as the baseline method for two tracks. For the final testing phase of both tracks, six teams submitted fact sheets and executable code of their solutions. This report brings together descriptions and discussions of all these solutions. Both tracks use the same data and the datasets are available at this url. © 2023 ieee.

关键词： Image enhancement

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Masked Image Training for Generalizable Deep Image Denoising

Masked Image Training for Generalizable Deep Image Denoising

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

作者： Chen, Haoyu Gu, Jinjin Liu, Yihao Magid, Salma Abdel Dong, Chao Wang, Qiong Pfister, Hanspeter Zhu, Lei Hong Kong Univ Sci & Technol Guangzhou Hong Kong Peoples R China Shanghai AI Lab Shanghai Peoples R China Univ Sydney Sydney Australia Chinese Acad Sci Shenzhen Inst Adv Technol ShenZhen Key Lab Comp Vis & Pattern Recognit Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Guangdong Prov Key Lab Comp Vision & Virtual Rea Beijing Peoples R China Harvard Univ Cambridge MA USA Hong Kong Univ Sci & Technol Hong Kong Peoples R China

ISBN: (纸本)9798350301298

When capturing and storing images, devices inevitably introduce noise. Reducing this noise is a critical task called image denoising. Deep learning has become the de facto method for image denoising, especially with the emergence of Transformer-based models that have achieved notable state-of-the-art results on various image tasks. However, deep learning-based methods often suffer from a lack of generalization ability. For example, deep models trained on Gaussian noise may perform poorly when tested on other noise distributions. To address this issue, we present a novel approach to enhance the generalization performance of denoising networks, known as masked training. Our method involves masking random pixels of the input image and reconstructing the missing information during training. We also mask out the features in the self-attention layers to avoid the impact of training-testing inconsistency. Our approach exhibits better generalization ability than other deep learning models and is directly applicable to real-world scenarios. Additionally, our interpretability analysis demonstrates the superiority of our method.

关键词： Low-level vision

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CiCo: Domain-Aware Sign Language Retrieval via Cross-Lingual Contrastive Learning

CiCo: Domain-Aware Sign Language Retrieval via Cross-Lingual...

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

作者： Cheng, Yiting Wei, Fangyun Bao, Jianmin Chen, Dong Zhang, Wenqiang Fudan Univ Sch Comp Sci Shanghai Peoples R China Microsoft Res Asia Beijing Peoples R China

ISBN: (纸本)9798350301298

This work focuses on sign language retrieval-a recently proposed task for sign language understanding. Sign language retrieval consists of two sub-tasks: text-to-sign-video (T2V) retrieval and sign-video-to-text (V2T) retrieval. Different from traditional video-text retrieval, sign language videos, not only contain visual signals but also carry abundant semantic meanings by themselves due to the fact that sign languages are also natural languages. Considering this character, we formulate sign language retrieval as a cross-lingual retrieval problem as well as a video-text retrieval task. Concretely, we take into account the linguistic properties of both sign languages and natural languages, and simultaneously identify the fine-grained cross-lingual (i.e., sign-to-word) mappings while contrasting the texts and the sign videos in a joint embedding space. This process is termed as cross-lingual contrastive learning. Another challenge is raised by the data scarcity issue-sign language datasets are orders of magnitude smaller in scale than that of speech recognition. We alleviate this issue by adopting a domain-agnostic sign encoder pre-trained on large-scale sign videos into the target domain via pseudo-labeling. Our framework, termed as domain-aware sign language retrieval via Cross-lingual Contrastive learning or CiCo for short, outperforms the pioneering method by large margins on various datasets, e.g., +22.4 T2V and +28.0 V2T R@1 improvements on How2Sign dataset, and +13.7 T2V and +17.1 V2T R@1 improvements on PHOENIX-2014T dataset. Code and models are available at: https://***/FangyunWei/SLRT.

关键词： vision applications and systems

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NTIRE 2022 Challenge on Night Photography Rendering

NTIRE 2022 Challenge on Night Photography Rendering

引用

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

作者： Ershov, Egor Savchik, Alex Shepelev, Denis Banic, Nikola Brown, Michael S. Timofte, Radu Koscevic, Karlo Freeman, Michael Tesalin, Vasily Bocharov, Dmitry Semenkov, Illya Subasic, Marko Loncaric, Sven Terekhin, Arseniy Liu, Shuai Feng, Chaoyu Wang, Hao Zhu, Ran Li, Yongqiang Lei, Lei Li, Zhihao Yi, Si Han, Ling-Hao Wu, Ruiqi Jin, Xin Guo, Chunle Kinli, Furkan Mentes, Sami Ozcan, Baris Kirac, Furkan Zini, Simone Rota, Claudio Buzzelli, Marco Bianco, Simone Schettini, Raimondo Li, Wei Ma, Yipeng Wang, Tao Xu, Ruikang Song, Fenglong Chen, Wei-Ting Yang, Hao-Hsiang Huang, Zhi-Kai Chang, Hua-En Kuo, Sy-Yen Liang, Zhexin Zhou, Shangchen Feng, Ruicheng Li, Chongyi Chen, Xiangyu Song, Binbin Zhang, Shile Liu, Lin Wang, Zhendong Ryu, Dohoon Bae, Hyokyoung Kwon, Taesung Desai, Chaitra Akalwadi, Nikhil Joshi, Amogh Mandi, Chinmayee Malagi, Sampada Uppin, Akash Reddy, Sai Sudheer Tabib, Ramesh Ashok Patil, Ujwala Mudenagudi, Uma Kharkevich Inst Inst Informat Transmiss Problems Moscow Russia Gideon Bros Osijek Croatia York Univ Toronto ON Canada Swiss Fed Inst Technol Zurich Zurich Switzerland Univ Wurzburg Wurzburg Germany Univ Zagreb Fac Elect Engn & Comp Zagreb Croatia Michael Freeman Photog London England Xiaomi Inc Beijing Peoples R China Nanjing Univ Nanjing Peoples R China Nankai Univ Tianjin Peoples R China Wuhan Univ Technol Wuhan Peoples R China Ozyegin Univ Istanbul Turkey Univ Milano Bicocca Milan Italy Huawei Noahs Ark Lab Montreal PQ Canada Natl Taiwan Univ Grad Inst Elect Engn New Taipei Taiwan Natl Taiwan Univ Dept Elect Engn New Taipei Taiwan Nanyang Technol Univ Singapore Singapore Univ Macau Taipa Macao Peoples R China Univ Sci & Technol China Hefei Peoples R China Korea Adv Inst Sci & Technol Daejeon South Korea KLE Technol Univ Ctr Excellence Visual Intelligence CEVI Hubballi Karnataka India

ISBN: (纸本)9781665487399

This paper reviews the NTIRE 2022 challenge on night photography rendering. The challenge solicited solutions that processed RAW camera images captured in night scenes to produce a photo-finished output image encoded in the standard RGB (sRGB) space. Given the subjective nature of this task, the proposed solutions were evaluated based on the mean opinions of viewers asked to judge the visual appearance of the results. Michael Freeman, a world-renowned photographer, further ranked the solutions with the highest mean opinion scores. A total of 13 teams competed in the final phase of the challenge. The proposed methods provided by the participating teams represent state-of-the-art performance in nighttime photography. Results from the various teams can be found here: https://***/

关键词： Photography Visualization computer vision conferences Rendering (computer graphics) Cameras pattern recognition

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PanoDR: Spherical Panorama Diminished Reality for Indoor Scenes

PanoDR: Spherical Panorama Diminished Reality for Indoor Sce...

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

作者： Gkitsas, Vasileios Sterzentsenko, Vladimiros Zioulis, Nikolaos Albanis, Georgios Zarpalas, Dimitrios Ctr Res & Technol Hellas Thessaloniki Greece

ISBN: (纸本)9781665448994

The rising availability of commercial 360 degrees cameras that democratize indoor scanning, has increased the interest for novel applications, such as interior space re-design. Diminished Reality (DR) fulfills the requirement of such applications, to remove existing objects in the scene, essentially translating this to a counterfactual inpainting task. While recent advances in data-driven inpainting have shown significant progress in generating realistic samples, they are not constrained to produce results with reality mapped structures. To preserve the 'reality' in indoor (re-)planning applications, the scene's structure preservation is crucial. To ensure structure-aware counterfactual inpainting, we propose a model that initially predicts the structure of a indoor scene and then uses it to guide the reconstruction of an empty - background only - representation of the same scene. We train and compare against other state-of-the-art methods on a version of the Structured3D dataset [47] modified for DR, showing superior results in both quantitative metrics and qualitative results, but more interestingly, our approach exhibits a much faster convergence rate. Code and models are available at ***/VCL3D/PanoDR/

关键词： Measurement Image segmentation computer vision conferences Predictive models Planning pattern recognition

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A Deep Adversarial Framework for Visually Explainable Periocular recognition

A Deep Adversarial Framework for Visually Explainable Perioc...

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

作者： Brito, Joao Proenca, Hugo Univ Beira Interior IT Inst Telecomunicacoes P-6200001 Covilha Portugal

ISBN: (纸本)9781665448994

In the biometrics context, the ability to provide the reasoning behind a decision has been at the core of major research efforts. Explanations serve not only to increase the trust amongst the users of a system, but also to augment the system's overall accountability and transparency. In this work, we describe a periocular recognition framework that not only performs biometric recognition, but also provides visual representations of the features/regions that supported a decision. Being particularly designed to explain non-match ("impostors") decisions, our solution uses adversarial generative techniques to synthesise a large set of "genuine" image pairs, from where the most similar elements with respect to a query are retrieved. Then, assuming the alignment between the query/retrieved pairs, the element-wise differences between the query and a weighted average of the retrieved elements yields a visual explanation of the regions in the query pair that would have to be different to transform it into a "genuine" pair. Our quantitative and qualitative experiments validate the proposed solution, yielding recognition rates that are similar to the state-of-the-art, but - most importantly - also providing the visual explanations for every decision.

关键词： Visualization computer vision Biometrics (access control) conferences Transforms Cognition pattern recognition

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On Disentanglement and Mutual Information in Semi-Supervised Variational Auto-Encoders

On Disentanglement and Mutual Information in Semi-Supervised...

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

作者： Gordon Rodriguez, Elliott Columbia Univ Dept Stat New York NY 10027 USA

ISBN: (纸本)9781665448994

In the context of variational auto-encoders, learning disentangled latent variable representations remains a challenging problem. In this abstract, we consider the semi-supervised setting, in which the factors of variation are labelled for a small fraction of our samples. We examine how the quality of learned representations is affected by the dimension of the unsupervised component of the latent space. We also consider a variational lower bound for the mutual information between the data and the semi-supervised component of the latent space, and analyze its role in the context of disentangled representation learning.

关键词： computer vision conferences pattern recognition Mutual information

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NTIRE 2022 Challenge on Perceptual Image Quality Assessment

NTIRE 2022 Challenge on Perceptual Image Quality Assessment

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

作者： Gu, Jinjin Cai, Haoming Dong, Chao Ren, Jimmy S. Timofte, Radu Gong, Yuan Lao, Shanshan Shi, Shuwei Wang, Jiahao Yang, Sidi Wu, Tianhe Xia, Weihao Yang, Yujiu Cao, Mingdeng Heng, Cong Fu, Lingzhi Zhang, Rongyu Zhang, Yusheng Wang, Hao Song, Hongjian Wang, Jing Fan, Haotian Hou, Xiaoxia Sun, Ming Li, Mading Zhao, Kai Yuan, Kun Kong, Zishang Wu, Mingda Zheng, Chuanchuan Conde, Marcos, V Burchi, Maxime Feng, Longtao Zhang, Tao Li, Yang Xu, Jingwen Wang, Haiqiang Liao, Yiting Li, Junlin Xu, Kele Sun, Tao Xiong, Yunsheng Keshari, Abhisek Komal, Komal Thakur, Sadbhawana Jakhetiya, Vinit Subudhi, Badri N. Yang, Hao-Hsiang Chang, Hua-En Huang, Zhi-Kai Chen, Wei-Ting Kuo, Sy-Yen Dutta, Saikat Das, Sourya Dipta Shah, Nisarg A. Tiwari, Anil Kumar Shanghai AI Lab Shanghai Peoples R China Univ Sydney Sch Elect & Informat Engn Sydney NSW Australia Chinese Acad Sci Shenzhen Inst Adv Technol Beijing Peoples R China SenseTime Res Beijing Peoples R China Univ Wurzburg Wurzburg Germany Swiss Fed Inst Technol Zurich Switzerland Tsinghua Univ Tsinghua Shenzhen Int Grad Sch Beijing Peoples R China Tsinghua Univ Dept Automat Beijing Peoples R China UCL London England Netease Hangzhou Peoples R China Kuaishou Beijing Peoples R China Univ Wurzburg Comp Vis Lab Wurzburg Germany ByteDance Beijing Peoples R China Key Lab Parallel & Distributed Proc Beijing Peoples R China Indian Inst Technol Jammu Jagti Jammu & Kashmir India Indian Inst Technol Madras Chennai Tamil Nadu India Jadavpur Univ Kolkata W Bengal India Indian Inst Technol Jodhpur Jodhpur Rajasthan India Natl Taiwan Univ New Taipei Taiwan

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

This paper reports on the NTIRE 2022 challenge on perceptual image quality assessment (IQA), held in conjunction with the New Trends in Image Restoration and Enhancement workshop (NTIRE) workshop at CVPR 2022. This challenge is held to address the emerging challenge of IQA by perceptual image processing algorithms. The output images of these algorithms have completely different characteristics from traditional distortions and are included in the PIPAL dataset used in this challenge. This challenge is divided into two tracks, a full-reference IQA track similar to the previous NTIRE IQA challenge and a new track that focuses on the no-reference IQA methods. The challenge has 192 and 179 registered participants for two tracks. In the final testing stage, 7 and 8 participating teams submitted their models and fact sheets. Almost all of them have achieved better results than existing IQA methods, and the winning method can demonstrate state-of-the-art performance.

关键词： Image quality computer vision conferences Computational modeling Market research Distortion pattern recognition

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