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检索条件"任意字段=2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2023"

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Pose-Guided R-CNN for Jersey Number recognition in Sports

Pose-Guided R-CNN for Jersey Number Recognition in Sports

引用

ieee/cvf conference on computer vision and pattern recognition workshops

作者： Hengyue Liu Bir Bhanu Center for Research in Intelligent Systems University of California

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125077

Recognizing player jersey number in sports match video streams is a challenging computer vision task. The human pose and view-point variations displayed in frames lead to many difficulties in recognizing the digits on jerseys. These challenges are addressed here using an approach that exploits human body part cues with a Region-based Convolutional Neural Network (R-CNN) variant for digit level localization and classification. The paper first adopts the Region Proposal Network (RPN) to perform anchor classification and bounding-box regression over three classes: background, person and digit. The person and digit proposals are geometrically related and fed to a network classifier. Subsequently, it introduces a human body key-point prediction branch and a pose-guided regressor to get better bounding-box offsets for generating digit proposals. A novel dataset of soccer-match video frames with corresponding multi-digit class labels, player and jersey number bounding boxes, and single digit segmentation masks is collected. Our framework outperforms all existing models on jersey number recognition task. This work will be essential to the automation of player identification across multiple sports, and releasing the dataset will ease future research on sports video analysis.

关键词： Sports Digit bounding boxes Persons Digital HUMAN BODY PARTS images computer vision VIDEO ANALYSIS Automation FRAMES

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Variational Learning of Beta-Liouville Hidden Markov Models for Infrared Action recognition

Variational Learning of Beta-Liouville Hidden Markov Models ...

引用

ieee/cvf conference on computer vision and pattern recognition workshops

作者： Samr Ali Nizar Bouguila Electrical and Computer Engineering Department Concordia University Concordia Institute for Information Systems Engineering Concordia University

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125077

Infrared (IR) images are characterized by a lower sensitivity to lighting conditions than the visible spectrum. This opens the door to relatively untapped research potential of automatic recognition systems that are robust to shadows and variability in illumination levels or appearance. IR action recognition (AR) is one such application. It remains a fairly unexplored domain in IR. As such, in this paper, we propose the use of hidden Markov models (HMM) for IR AR. We also derive the mathematical model for the variational learning of Beta-Liouville (BL) HMMs. Next, we present the results of the proposed model on the Infrared Action recognition (InfAR) dataset. To the best of our knowledge, this is the first application of HMMs to AR in the IR domain, and the first application of the BL HMMs to AR. Experimental results demonstrate promising results using different features extracted from the InfAR dataset.

关键词： Hidden Markov models Argon automated identification system iridium illumination level Infrared imaging Infrared radiation shadows recognition (Psychology) Learning Mathematical Model

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Efficient Semantic Segmentation using Gradual Grouping 31

Efficient Semantic Segmentation using Gradual Grouping

引用

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

作者： Vallurupalli, Nikitha Annamaneni, Sriharsha Varma, Girish Jawahar, C., V Mathew, Manu Nagori, Soyeb IIIT Hyderabad Kohli Ctr Intelligent Syst Ctr Visual Informat Technol Hyderabad Telangana India Texas Instruments Inc Bangalore Karnataka India

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Deep CNNs for semantic segmentation have high memory and run time requirements. Various approaches have been proposed to make CNNs efficient like grouped, shuffled, depth-wise separable convolutions. We study the effectiveness of these techniques on a real-time semantic segmentation architecture like ERFNet for improving run-time by over 5X. We apply these techniques to CNN layers partially or fully and evaluate the testing accuracies on Cityscapes dataset. We obtain accuracy vs parameters/FLOPs trade offs, giving accuracy scores for models that can run under specified runtime budgets. We further propose a novel training procedure which starts out with a dense convolution but gradually evolves towards a grouped convolution. We show that our proposed training method and efficient architecture design can improve accuracies by over 8% with depthwise separable convolutions applied on the encoder of ERFNet and attaching a light weight decoder. This results in a model which has a 5X improvement in FLOPs while only suffering a 4% degradation in accuracy with respect to ERFNet.

关键词： Training Semantics computer architecture Computational modeling Sparse matrices Predictive models Decoding

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Live Demonstration: Digit recognition On Pixel Processor Arrays

Live Demonstration: Digit Recognition On Pixel Processor Arr...

引用

ieee/cvf conference on computer vision and pattern recognition workshops

作者： Laurie Bose Jianing Chen Stephen J. Carey Piotr Dudek Walterio Mayol-Cuevas Faculty of Engineering Computer Science University of Bristol School of Electrical and Electronic Engineering The University of Manchester

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125077

In this demo, we will showcase recent work on implementing convolutional neural networks directly on pixel processor arrays (PPA). As CNNs demonstrate enhanced performance across tasks from classification to image synthesis, it becomes essential to find the most adequate ways to realize them, especially for embedded, real-time and reactive tasks in areas across computer vision and Robotics. The PPA concept is one architecture that pairs sensing and massively parallel processing at the focal plane level and allow mid to high level tasks to be run wholly embedded within them. They allow operation at high framerates and low energy consumption (≤ 2W), and without the need for external signal interpretation or processing. In this demo we will showcase our recent work on the implementation of CNNs on the SCAMP5 architecture as a step towards true end-to-end operation on flexibly programmable PPA hardware. In particular, we will showcase handwritten number classification from image capture to classification wholly embedded on the PPA.

关键词： Religious Missions Image synthesis image capture Taxonomy images computer vision Digit embedding Surgery massively parallel processing work implement low energy consumption Digital external signals

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Filter Guided Manifold Optimization in the Autoencoder Latent Space

Filter Guided Manifold Optimization in the Autoencoder Laten...

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ieee/cvf conference on computer vision and pattern recognition workshops

作者： Nate Lannan Guoliang Fan School of Electrical and Computer Engineering Oklahoma State University

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125077

An autoencoder is a class of neural network that is trained to output an accurate reproduction of the input while learning key lower dimensional features, other-wise known as a manifold. A lower dimensional representation of the original input, referred to as the latent space, encodes the intrinsic data structure over the manifold. This paper proposes filter-guided manifold optimization in the latent space of a convolutional autoencoder to recover noisy motion data collected by a depth sensor. Autoencoder output is smoothed using four traditional filters and employed as target motion data in an objective function. The difference between the actual output and target is minimized through stochastic gradient descent over the latent space, using manifold optimization to produce the expected smooth output. The advantage of this filter-guided approach over-traditional filtering is that the resultant motion data still adheres to the manifold in the latent space learned by the autoencorder from training on motion data.

关键词： Manifolds Filters

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DeepGlobe 2018: A Challenge to Parse the Earth through Satellite Images 31

DeepGlobe 2018: A Challenge to Parse the Earth through Satel...

引用

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

作者： Demir, Ilke Koperski, Krzysztof Lindenbaum, David Pang, Guan Huang, Jing Bast, Saikat Hughes, Forest Tuia, Devis Raskar, Ramesh Facebook Menlo Pk CA 94025 USA DigitalGlobe Westminster CO USA CosmiQ Works Burnaby BC Canada Wageningen Univ Wageningen Netherlands MIT Media Lab Cambridge MA 02139 USA

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

We present the DeepGlobe 2018 Satellite Image Understanding Challenge, which includes three public competitions for segmentation, detection, and classification tasks on satellite images (Figure 1). Similar to other challenges in computer vision domain such as DAVIS[21] and COCO[33], DeepGlobe proposes three datasets and corresponding evaluation methodologies, coherently bundled in three competitions with a dedicated workshop co-located with CVPR 2018. We observed that satellite imagery is a rich and structured source of information, yet it is less investigated than everyday images by computer vision researchers. However, bridging modern computer vision with remote sensing data analysis could have critical impact to the way we understand our environment and lead to major breakthroughs in global urban planning or climate change research. Keeping such bridging objective in mind, DeepGlobe aims to bring together researchers from different domains to raise awareness of remote sensing in the computer vision community and vice-versa. We aim to improve and evaluate state-of-the-art satellite image understanding approaches, which can hopefully serve as reference benchmarks for future research in the same topic. In this paper, we analyze characteristics of each dataset, define the evaluation criteria of the competitions, and provide baselines for each task.

关键词： Satellites Roads Buildings Task analysis Urban areas computer vision Image segmentation

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A Customized Camera Imaging Pipeline for Dermatological Imaging

A Customized Camera Imaging Pipeline for Dermatological Imag...

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ieee/cvf conference on computer vision and pattern recognition workshops

作者： Hakki Can Karaimer Iman Khodadad Farnoud Kazemzadeh Michael S. Brown York University Elucid Labs

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125077

This paper describes the customization of the camera processing pipeline of a machine vision camera that has been integrated into a hand-held dermatological imaging device. The device uses a combination of visible and non-visible spectral LEDs to allow capture of visible RGB imagery as well as selected non-visible wavelengths. Our customization involves two components. The first component is a color calibration procedure that ensures the captured images are colorimetrically more accurate than those obtained through the machine vision camera's native API. The need for color calibration is a critical component that is often overlooked or poorly understood by computer vision engineers. Our second component is a fast method to integrate the narrow band spectral images (some of which are outside the visible range) into the visible RGB image for enhanced visualization. This component of our pipeline involves evaluating several algorithms capable of multiple image fusion to determine the most suitable one for our application. Quantitative and subject results, including feedback from clinicians, demonstrate the effectiveness of our customization procedure.

关键词： Customization COLOR CORRECTION pipeline images Cameras robot vision Shortcuts computer vision Crucial components red-green-blue colour model image fusion Imaging Feedback

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SUSiNet: See, Understand and Summarize it

SUSiNet: See, Understand and Summarize it

引用

ieee/cvf conference on computer vision and pattern recognition workshops

作者： Petros Koutras Petros Maragos National Technical University of Athens Greece

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125077

In this work we propose a multi-task spatio-temporal network, called SUSiNet, that can jointly tackle the spatio-temporal problems of saliency estimation, action recognition and video summarization. Our approach employs a single network that is jointly end-to-end trained for all tasks with multiple and diverse datasets related to the exploring tasks. The proposed network uses a unified architecture that includes global and task specific layer and produces multiple output types, i.e., saliency maps or classification labels, by employing the same video input. Moreover, one additional contribution is that the proposed network can be deeply supervised through an attention module that is related to human attention as it is expressed by eye-tracking data. From the extensive evaluation, on seven different datasets, we have observed that the multi-task network performs as well as the state-of-the-art single-task methods (or in some cases better), while it requires less computational budget than having one independent network per each task.

关键词： Task analysis Three-dimensional displays Visualization Estimation Two dimensional displays Training computer architecture three-dimensional displays task analysis computer Architecture Visualization Training Functional training Religious Missions

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FACSCaps: Pose-Independent Facial Action Coding with Capsules 31

FACSCaps: Pose-Independent Facial Action Coding with Capsule...

引用

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

作者： Ertugrul, Itir Onal Jeni, Laszlo A. Cohn, Jeffrey F. Carnegie Mellon Univ Robot Inst Pittsburgh PA 15213 USA Univ Pittsburgh Dept Psychol Pittsburgh PA 15260 USA

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Most automated facial expression analysis methods treat the face as a 2D object, flat like a sheet of paper. That works well provided images are frontal or nearly so. In real-world conditions, moderate to large head rotation is common and system performance to recognize expression degrades. Multi-view Convolutional Neural Networks (CNNs) have been proposed to increase robustness to pose, but they require greater model sizes and may generalize poorly across views that are not included in the training set. We propose FACSCaps architecture to handle multi-view and multi-label facial action unit (AU) detection within a single model that can generalize to novel views. Additionally, FACSCaps's ability to synthesize faces enables insights into what is leaned by the model. FACSCaps models video frames using matrix capsules, where hierarchical pose relationships between face parts are built into internal representations. The model is trained by jointly optimizing a multi-label loss and the reconstruction accuracy. FACSCaps was evaluated using the FERA 2017 facial expression dataset that includes spontaneous facial expressions in a wide range of head orientations. FACSCaps outperformed both state-of-the-art CNNs and their temporal extensions.

关键词： Gold Image reconstruction computer architecture Face Training Feature extraction Routing

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Human Pose as Calibration pattern;3D Human Pose Estimation with Multiple Unsynchronized and Uncalibrated Cameras 31

Human Pose as Calibration Pattern;3D Human Pose Estimation w...

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

作者： Takahashi, Kosuke Mikami, Dan Isogawa, Mariko Kimata, Hideaki NIPPON TELEGRAPH & TEL COORPORAT NTT Media Intelligence Labs Tokyo Japan

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

This paper proposes a novel algorithm of estimating 3D human pose from multi-view videos captured by unsynchronized and uncalibrated cameras. In a such configuration, the conventional vision-based approaches utilize detected 2D features of common 3D points for synchronization and camera pose estimation, however, they sometimes suffer from difficulties of feature correspondences in case of wide baselines. For such cases, the proposed method focuses on that the projections of human joints can be associated each other robustly even in wide baseline videos and utilizes them as the common reference points. To utilize the projections of joint as the corresponding points, they should be detected in the images, however, these 2D joint sometimes include detection errors which make the estimation unstable. For dealing with such errors, the proposed method introduces two ideas. The first idea is to relax the reprojection errors for avoiding optimizing to noised observations. The second idea is to introduce an geometric constraint on the prior knowledge that the reference points consists of human joints. We demonstrate the performance of the proposed algorithm of synchronization and pose estimation with qualitative and quantitative evaluations using synthesized and real data.

关键词： Cameras Two dimensional displays Three-dimensional displays Pose estimation Videos Calibration

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