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检索条件"任意字段=IEEE Conference on Computer Vision and Pattern Recognition"

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Research on computer vision System for Intelligent Management of Football Stadium Based on Internet of Things 2

Research on Computer Vision System for Intelligent Managemen...

引用

2nd ieee International conference on Sensors, Electronics and computer Engineering, ICSECE 2024

作者： Zhang, Yongxiang Wuchang Shouyi University Wuhan China

ISBN: (纸本)9798350373646

This paper proposes an intelligent management computer vision system based on the Internet of Things for the special needs of football stadiums. The system integrates advanced image processing algorithms and computer system architecture, aiming to realize the functions of all-round monitoring, audience behavior analysis and security early warning inside and outside the football stadium. The core of the system includes a network of high-definition cameras, edge computing nodes and a central server. Cameras are responsible for capturing the environment inside and outside the venue in real time, and initial data processing is carried out through edge computing nodes to reduce data transmission latency. The central server is equipped with advanced image recognition algorithms, which can identify player movements, spectator density distribution, and abnormal behavior, such as trespassing, crowd gathering, etc., and issue instant alerts. In addition, the system also has automated ticket management, facility maintenance reminders and energy monitoring functions. The experimental results show that the system significantly improves the management efficiency and safety of football stadiums, especially during large-scale events, and can effectively prevent and respond to various emergencies. At the same time, through intelligent analysis of audience behavior, the system can also provide decision support for venue managers, optimize resource allocation and enhance the spectator experience. © 2024 ieee.

关键词： Resource allocation

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Deep Learning Character recognition of Handwritten Devanagari Script: A Complete Survey 1

Deep Learning Character Recognition of Handwritten Devanagar...

引用

1st ieee International conference on Contemporary Computing and Communications, InC4 2023

作者： Sharma, Arpit Mithun, B.N. Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350335774

recognition of handwritten characters is a concept in which the single characters are classified, it is a facility of an electronic device to scan and decipher the handwritten input from a variety of sources, including written texts, images, and other digital touch-screen devices. This concept is being used in distinctive sectors such as the processing of bank checks, form data entry, and parcel posting and nowadays it is becoming a very important issue in the pattern recognition domain and a very challenging task to resolve it. Since deep learning is a crucial strategy in solving detection and pattern recognition problems, several algorithms are available to classify the characters with better prediction rates on different datasets, and ultimately, whichever algorithm gives the optimized results will be considered the best solution for the character recognition problem. As a result, various solutions proposed by the existing researchers are discussed using deep learning algorithms in this survey article. © 2023 ieee.

关键词： Character recognition

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VISUALVOICE: Audio-Visual Speech Separation with Cross-Modal Consistency

VISUALVOICE: Audio-Visual Speech Separation with Cross-Modal...

引用

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

作者： Gao, Ruohan Grauman, Kristen Univ Texas Austin Austin TX 78712 USA Stanford Univ Stanford CA 94305 USA Facebook AI Res Menlo Pk CA USA

ISBN: (纸本)9781665445092

We introduce a new approach for audio-visual speech separation. Given a video, the goal is to extract the speech associated with a face in spite of simultaneous background sounds and/or other human speakers. Whereas existing methods focus on learning the alignment between the speaker's lip movements and the sounds they generate, we propose to leverage the speaker's face appearance as an additional prior to isolate the corresponding vocal qualities they are likely to produce. Our approach jointly learns audio-visual speech separation and cross-modal speaker embeddings from unlabeled video. It yields state-of-the-art results on five benchmark datasets for audiovisual speech separation and enhancement, and generalizes well to challenging real-world videos of diverse scenarios.

关键词： Location awareness computer vision Face recognition Lips Computational modeling Speech recognition Speech enhancement

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Private-Shared Disentangled Multimodal VAE for Learning of Latent Representations

Private-Shared Disentangled Multimodal VAE for Learning of L...

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

作者： Lee, Mihee Pavlovic, Vladimir Rutgers State Univ Piscataway NJ 08854 USA

ISBN: (纸本)9781665448994

Multi-modal generative models represent an important family of deep models, whose goal is to facilitate representation learning on data with multiple views or modalities. However, current deep multi-modal models focus on the inference of shared representations, while neglecting the important private aspects of data within individual modalities. In this paper, we introduce a disentangled multi-modal variational autoencoder (DMVAE) that utilizes disentangled VAE strategy to separate the private and shared latent spaces of multiple modalities. We demonstrate the utility of DMVAE two image modalities of MNIST and Google Street View House Number (SVHN) datasets as well as image and text modalities from the Oxford-102 Flowers dataset. Our experiments indicate the essence of retaining the private representation as well as the private-shared disentanglement to effectively direct the information across multiple analysis-synthesis conduits.

关键词： computer vision conferences Computational modeling Data models pattern recognition Internet Task analysis

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NUTA: Non-uniform Temporal Aggregation for Action recognition 22

NUTA: Non-uniform Temporal Aggregation for Action Recognitio...

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22nd ieee/CVF Winter conference on Applications of computer vision (WACV)

作者： Li, Xinyu Liu, Chunhui Shuai, Bing Zhu, Yi Chen, Hao Tighe, Joseph Amazon Web Serv Seattle WA 98109 USA

ISBN: (纸本)9781665409155

In the world of action recognition research, one primary focus has been on how to construct and train networks to model the spatial-temporal volume of an input video. These methods typically uniformly sample a segment of an input clip (along the temporal dimension). However, not all parts of a video are equally important to determine the action in the clip. In this work, we focus instead on learning where to extract features, so as to focus on the most informative parts of the video. We propose a method called the non-uniform temporal aggregation (NUTA), which aggregates features only from informative temporal segments. We also introduce a synchronization method that allows our NUTA features to be temporally aligned with traditional uniformly sampled video features, so that both local and clip-level features can be combined. Our model has achieved state-of-the-art performance on four widely used large-scale action-recognition datasets (Kinetics400, Kinetics700, Something-something V2 and Charades). In addition, we have created a visualization to illustrate how the proposed NUTA method selects only the most relevant parts of a video clip.

关键词： Visualization Solid modeling computer vision Aggregates Feature extraction Synchronization Task analysis

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Image Super-Resolution with Non-Local Sparse Attention

Image Super-Resolution with Non-Local Sparse Attention

引用

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

作者： Mei, Yiqun Fan, Yuchen Zhou, Yuqian Univ Illinois Champaign IL 61820 USA

ISBN: (纸本)9781665445092

Both Non-Local (NL) operation and sparse representation are crucial for Single Image Super-Resolution (SISR). In this paper, we investigate their combinations and propose a novel Non-Local Sparse Attention (NLSA) with dynamic sparse attention pattern. NLSA is designed to retain long-range modeling capability from NL operation while enjoying robustness and high-efficiency of sparse representation. Specifically, NLSA rectifies non-local attention with spherical locality sensitive hashing (LSH) that partitions the input space into hash buckets of related features. For every query signal, NLSA assigns a bucket to it and only computes attention within the bucket. The resulting sparse attention prevents the model from attending to locations that are noisy and less-informative, while reducing the computational cost from quadratic to asymptotic linear with respect to the spatial size. Extensive experiments validate the effectiveness and efficiency of NLSA. With a few non-local sparse attention modules, our architecture, called non-local sparse network (NLSN), reaches state-of-the-art performance for SISR quantitatively and qualitatively.

关键词： computer vision Computational modeling Superresolution computer architecture Benchmark testing Robustness pattern recognition

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Few-Shot Open-Set recognition of Hyperspectral Images with Outlier Calibration Network 22

Few-Shot Open-Set Recognition of Hyperspectral Images with O...

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22nd ieee/CVF Winter conference on Applications of computer vision (WACV)

作者： Pal, Debabrata Bundele, Valay Sharma, Renuka Banerjee, Biplab Jeppu, Yogananda Indian Inst Technol Bombay Maharashtra India Honeywell Technol Solut Bengaluru India

ISBN: (纸本)9781665409155

We tackle the few-shot open-set recognition (FSOSR) problem in the context of remote sensing hyperspectral image (HSI) classification. Prior research on OSR mainly considers an empirical threshold on the class prediction scores to reject the outlier samples. Further, recent endeavors in few-shot HSI classification fail to recognize outliers due to the `closed-set' nature of the problem and the fact that the entire class distributions are unknown during training. To this end, we propose to optimize a novel outlier calibration network (OCN) together with a feature extraction module during the meta-training phase. The feature extractor is equipped with a novel residual 3D convolutional block attention network (R3CBAM) for enhanced spectral-spatial feature learning from HSI. Our method rejects the outliers based on OCN prediction scores barring the need for manual thresholding. Finally, we propose to augment the query set with synthesized support set features during the similarity learning stage in order to combat the data scarcity issue of few-shot learning. The superiority of the proposed model is showcased on four benchmark HSI datasets.(1)

关键词： Training Representation learning computer vision Image recognition Three-dimensional displays Manuals Benchmark testing

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Exploration of Machine Learning Attacks in Automotive Systems Using Physical and Mixed Reality Platforms

Exploration of Machine Learning Attacks in Automotive System...

引用

ieee International conference on Consumer Electronics (ICCE)

作者： Chamarthi, Venkata Sai Gireesh Chen, Xiangru Ravi, Bhagawat Baanav Yedla Ray, Sandip Univ Florida Dept ECE Gainesville FL 32611 USA

ISBN: (纸本)9781665491303

Adversarial attacks on Deep Neural Networks represent a critical challenge in the adoption of DNNs in critical applications. However, - and in spite of its great need, - there is significant mystery surrounding attacks on DNNs. One reason for this is the lack of a platform that enables users to get a hands-on, intuitive understanding of the attacks. In this paper, we address this problem by designing an extensible, configurable exploration platform for studying various attacks on DNNs. Our platform specifically focuses on DNNs deployed in computer vision modules of automotive systems. Using the platform, the user can perform various adversarial machine learning attacks, such as evasion attacks and image-perturbation attacks, and comprehend their adversarial effects on autonomous vehicles. The platform can be used to plug and play with various neural network models developed for Traffic Sign recognition systems in autonomous vehicles. The infrastructure includes both physical and mixed-reality variants, and we demonstrate the usage of the platform on two traffic sign recognition models with different adversarial attacks.

关键词： Deep learning Adaptation models Machine vision Neural networks Mixed reality Virtual reality Adversarial machine learning

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Authentication and Verification in Human-Robot Cooperative Robotic Cells using Stereo vision and Gesture Control 6

Authentication and Verification in Human-Robot Cooperative R...

引用

6th ieee International conference on Image Processing, Applications and Systems, IPAS 2025

作者： Kovacs, Gabor Sziranyi, Tamas HUN-REN Institute for Computer Science and Control Machine Perception Research Laboratory Budapest Hungary

ISBN: (纸本)9798331506520

The integration of human-robot interaction (HRI) technologies with industrial automation has become increasingly essential for enhancing productivity and safety in manufacturing environments. In this paper, we propose a novel approach to address these challenges by using stereo vision and gesture control in cooperative robotic cells. Our system enables seamless authentication of operators and real-time verification of task execution, ensuring compliance with established protocols and safety *** features of our system include its gesture-based operation with gesture recognition algorithms, allowing operators to interact with robotic systems intuitively and efficiently. By leveraging stereo vision, our system accurately tracks the operators' movement within the workspace, facilitating precise task execution and object *** present a detailed description of our system architecture, experimental configuration, and real-world performance assessment. Our results demonstrate the effectiveness and feasibility of our approach in enhancing operational efficiency, ensuring quality, and improving the overall user experience in industrial automation. © 2025 ieee.

关键词： Human robot interaction

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Dressing in Order: Recurrent Person Image Generation for Pose Transfer, Virtual Try-on and Outfit Editing

Dressing in Order: Recurrent Person Image Generation for Pos...

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

作者： Cui, Aiyu McKee, Daniel Lazebnik, Svetlana Univ Illinois Champaign IL 61820 USA

ISBN: (纸本)9781665448994

We propose a flexible person generation framework called Dressing in Order (DiOr), which supports 2D pose transfer, virtual try-on, and several fashion editing tasks. The key to DiOr is a novel recurrent generation pipeline to sequentially put garments on a person, so that trying on the same garments in different orders will result in different looks. Our system can produce dressing effects not achievable by existing work, including different interactions of garments (e.g., wearing a top tucked into the bottom or over it), as well as layering of multiple garments of the same type (e.g., jacket over shirt over t-shirt). DiOr explicitly encodes the shape and texture of each garment, enabling these elements to be edited separately. Extensive evaluations show that DiOr outperforms other recent methods like ADGAN [18] in terms of output quality, and handles a wide range of editing functions for which there is no direct supervision.

关键词： computer vision Shape Image synthesis conferences Clothing Pipelines pattern recognition

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