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检索条件"任意字段=29th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016"

共 90 条记录，以下是1-10 订阅

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Report on the 29th ieee conference on computer vision and pattern recognition (cvpr 2016)

Report on the 29th IEEE conference on computer vision and pa...

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作者： Uranishi, Yuki Cybermedia Center Osaka University Osaka Japan

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Efficient Online Multi-Camera Tracking with Memory-Efficient Accumulated Appearance Features and Trajectory Validation

Efficient Online Multi-Camera Tracking with Memory-Efficient...

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

作者： Lap Quoc Tran Huan Duc Vi Asilla Tokyo Japan

ISBN: (纸本)9798350365474

Multi-camera tracking (MCT) plays a crucial role in various computer vision applications. However, accurate tracking of individuals across multiple cameras faces challenges, particularly with identity switches. In this paper, we present an efficient online MCT system that tackles these challenges through online processing. Our system leverages memory-efficient accumulated appearance features to provide stable representations of individuals across cameras and time. By incorporating trajectory validation using hierarchical agglomerative clustering (HAC) in overlapping regions, ID transfers are identified and rectified. Evaluation on the 2024 AI City Challenge Track 1 dataset [39] demonstrates the competitive performance of our system, achieving accurate tracking in both overlapping and non-overlapping camera networks. With a 40.3% HOTA score [29], our system ranked 9th in the challenge. the integration of trajectory validation enhances performance by 8% over the baseline, and the accumulated appearance features further contribute to a 17% improvement.

关键词： computer vision

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OSKDet: Orientation-sensitive Keypoint Localization for Rotated Object Detection

OSKDet: Orientation-sensitive Keypoint Localization for Rota...

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

作者： Lu, Dongchen Li, Dongmei Li, Yali Wang, Shengjin Tsinghua Univ Dept Elect Engn Beijing Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Rotated object detection is a challenging issue in computer vision field. Inadequate rotated representation and the confusion of parametric regression have been the bottleneck for high performance rotated detection. In this paper, we propose an orientation-sensitive keypoint based rotated detector OSKDet. First, we adopt a set of keypoints to represent the target and predict the keypoint heatmap on ROI to get the rotated box. By proposing the orientation-sensitive heatmap, OSKDet could learn the shape and direction of rotated target implicitly and has stronger modeling capabilities for rotated representation, which improves the localization accuracy and acquires high quality detection results. Second, we explore a new unordered keypoint representation paradigm, which could avoid the confusion of keypoint regression caused by rule based ordering. Furthermore, we propose a localization quality uncertainty module to better predict the classification score by the distribution uncertainty of keypoints heatmap. Experimental results on several public benchmarks show the state-of-the-art performance of OSKDet. Specifically, we achieve an AP of 80.91% on DOTA, 89.98% on HRSC2016, 97.27% on UCAS-AOD, and a F-measure of 92.18% on ICDAR2015, 81.43% on ICDAR2017, respectively.

关键词： Location awareness Heating systems computer vision Uncertainty Shape Object detection Detectors

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A Comprehensive Study of Optical Character recognition 29

A Comprehensive Study of Optical Character Recognition

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29th International conference on Mixed Design of Integrated Circuits and System (MIXDES)

作者： Raj, Ravi Kos, Andrzej AGH Univ Sci & Technol Fac Comp Sci Elect & Telecommun Al Adama Mickiewicza 30 PL-30059 Krakow Poland

ISBN: (数字)9788363578220

ISBN: (纸本)9781665461764

In recent decades recognition of characters has become a most important research topic for computer vision researchers or scientists. One of the major techniques for character recognition is optical character recognition (OCR) which plays a vital role in recent years in the development of various methodologies for the recognition of characters of several languages alphabets. Currently, OCR technology is utilized by most of the applications of scanning documents and makes them readable for the users such as google translate, which has been developed for translating the language from one language to the other language. But the rate of accuracy and timing to perform the specified task is still a problem. this paper presents a brief description of the OCR technology, the timeline of its development, some major applications of this technology, and its future perspective in our daily life. Moreover, this article provides an overview of this fascinating research topic for the early-stage researchers of computer vision.

关键词： OCR Artificial Intelligence computer vision pattern recognition Character recognition

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Implementation of a CRNN-based low-power keyword recognition system on FPGA 14

Implementation of a CRNN-based low-power keyword recognition...

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14th ieee International conference on ASIC, ASICON 2021

作者： Guo, Limo Lin, PengXu Guo, Lei Liu, Bo Southeast University School of Electronic Science and Engineering Nanjing210096 China

ISBN: (纸本)9781665411493

A low-power and high-precision reconfigurable processor based on optimized convolutional recurrent neural network is proposed for noise robust keyword recognition. In order to create a low-power and high-precision system, we implemented a reconfigurable CRNN and quantization network on FPGA, which greatly reduced the use of DSP, BRAM, LUT and other resources. Our system can identify some keywords, such as yes, no, down and up within 50ms, and at a signal-to-noise ratio of-5dB, the actual accuracy reaches 86.4%. © 2021 ieee.

关键词： Field programmable gate arrays (FPGA)

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An overview of robot vision

An overview of robot vision

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27th Southern African Universities Power Engineering conference (SAUPEC) / 11th Robotics and Mechatronics conference of South Africa (RobMech) / 29th Annual Symposium of pattern-recognition-Association-of-South-Africa (PRASA)

作者： van Eden, Beatrice Rosman, Benjamin CSIR Mobile Intelligent Autonomous Syst Pretoria South Africa Univ Witwatersrand Sch Comp Sci & Appl Math Johannesburg South Africa

ISBN: (纸本)9781728103693

Robot vision is an interdisciplinary field that deals with how robots can be made to gain high-level understanding from digital images or videos. Understanding an image at the pixel level often does not provide enough information for decision making and action taking. In this case, higher level semantic information that describes the image is required. this helps the robot to accomplish complex tasks that require visual understanding. For robots to add value they need to be sufficiently effective at executing tasks in different settings. Despite many impressive advances in robot vision, robots still lack the ability to function as humans do in complex environments. Importantly, this includes being able to interpret and understand the perceptual complexities of the world. Robot vision is dependant on ideas from both computer vision and machine learning. In this paper we provide a overview of the advances in these disciplines and how they contribute to robot vision.

关键词： Robot sensing systems Image classification Object recognition Object detection Visualization computer architecture

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MULTI-CLASS WEAthER CLASSIFICATION FROM STILL IMAGE USING SAID ENSEMBLE MEthOD

MULTI-CLASS WEATHER CLASSIFICATION FROM STILL IMAGE USING SA...

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作者： Ajayi, Gbeminiyi Oluwafemi Wang Zenghui Univ South Africa Elect & Min Dept Johannesburg South Africa

ISBN: (纸本)9781728103693

In the field of computer vision, multi-class outdoor weather classification is a difficult task to perform due to diversity and lack of distinct weather characteristic or features. this research proposed a novel framework for identifying different weather scenes from still images using heterogeneous ensemble methods. Our approach is based on a method called Selection Based on Accuracy Intuition and diversity (SAID) of stacked ensemble algorithms. this involves the extraction of histogram of features from different weather scenes. the blending and boosting of different weather features using stacked ensemble algorithms increases recognition rate of different weather conditions compared to other classification and ensemble methods. the paper presents academic and practitioners a new insight into diversity of heterogeneous ensemble methods for solving the challenges of weather recognition from still images.

关键词： Stacking ensemble ensemble diversity weather identification recognition Image classification

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Proceedings of the ieee computer Society conference on computer vision and pattern recognition

Proceedings of the IEEE Computer Society Conference on Compu...

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29th ieee conference on computer vision and pattern recognition, cvpr 2016

ISBN: (纸本)9781467388504

the proceedings contain 640 papers. the topics discussed include: generation and comprehension of unambiguous object description;image question answering using convolutional neural network with dynamic parameter prediction;neural module networks;learning to assign orientations to feature points;affinity CNN: learning pixel-centric pairwise relations for figure/ground embedding;occlusion boundary detection via deep exploration of context;exploit bounding box annotations for multi-label object recognition;MCMC shape sampling for image segmentation with nonparametric shape priors;learning action maps of large environments via first-person vision;sample and filter: nonparametric scene parsing via efficient filtering;training region-based object detectors with online hard example mining;learning with side information through modality hallucination;HyperNet: towards accurate region proposal generation and joint object detection;macroscopic interferometry: rethinking depth estimation with frequency-domain time-of-flight;ASP vision: optically computing the first layer of convolutional neural networks using angle sensitive pixels;hierarchical recurrent neural encoder for video representation with application to captioning;and from keyframes to key objects: video summarization by representative object proposal selection.

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Deep Residual Network with Enhanced Upscaling Module for Super-Resolution 31

Deep Residual Network with Enhanced Upscaling Module for Sup...

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

作者： Kim, Jun-Hyuk Lee, Jong-Seok Yonsei Univ Sch Integrated Technol Seoul South Korea

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Single image super-resolution (SR) have recently shown great performance thanks to the advances in deep learning. In the middle of the deep networks for SR, a part that increases image resolution is required, for which a sub-pixel convolution layer is known as an efficient way. However, we argue that the method has room for improvement, and propose an enhanced upscaling module (EUM), which achieves improvement by utilizing nonlinear operations and skip connections. Employing our proposed EUM, we propose a novel deep residual network for SR, called EUSR. Our proposed EUSR was ranked in the 9th place among 24 teams in terms of SSIM in track 1 of the NTIRE 2018 SR Challenge [25]. In addition, we experimentally show that EUSR has comparable performance on x2 and x4 SR for four benchmark datasets to the state-of-the-art methods, and outperforms them on a large scaling factor (x8).

关键词： Convolution Image resolution Feature extraction Training Image reconstruction computer vision Signal resolution

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ieee computer Society conference on computer vision and pattern recognition Workshops

IEEE Computer Society Conference on Computer Vision and Patt...

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29th ieee conference on computer vision and pattern recognition Workshops, cvprW 2016

ISBN: (纸本)9781467388504

the proceedings contain 192 papers. the topics discussed include: feature vector compression based on least error quantization;a comprehensive analysis of deep learning based representation for face recognition;two-stream CNNs for gesture-based verification and identification: learning user style;CALIPER: continuous authentication layered with integrated PKI encoding recognition;latent fingerprint image segmentation using fractal dimension features and weighted extreme learning machine ensemble;a comparison of human and automated face verification accuracy on unconstrained image sets;offline signature verification based on bag-of-VisualWords model using KAZE features and weighting schemes;implementation of fixed-length template protection based on homomorphic encryption with application to signature biometrics;seeing the forest from the trees: a holistic approach to near-infrared heterogeneous face recognition;a novel visualization tool for evaluating the accuracy of 3d sensing and reconstruction algorithms for automatic dormant pruning applications;a pointing gesture based egocentric interaction system: dataset, approach and application;multimodal multi-stream deep learning for egocentric activity recognition;sparse kernel machines for discontinuous registration and nonstationary regularization;and accurate small deformation exponential approximant to integrate large velocity fields: application to image registration.

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