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Mobile Exergames - Burn Calories While Playing Games on a Smartphone

Mobile Exergames - Burn Calories While Playing Games on a Sm...

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26th IEEE conference on computer vision and pattern recognition (CVPR)

作者： Buddharaju, Pradeep Pamidi, Naga Siva Chandra Prasad Univ Houston Clear Lake Dept Comp Sci Houston TX 77058 USA

ISBN: (纸本)9780769549903

Exergames combine exercising with game play by requiring the users to perform some kind of physical activity (and exercise) in order to score points in the game. In this paper, we present a novel mobile exergaming framework, which requires the users to physically move and jump in order to score points in a game that is played on a smartphone. Our system uses a custom designed Exercising Pad (called ExerPad) in order to track the user's physical movement, and then automatically updates the corresponding game character's position on the screen. The ExerPad contains different shaped images, which are captured from the smartphone's inbuilt camera, and are automatically detected by our shape detection algorithm. We also use the smartphone's inbuilt accelerometer and gyroscope to detect other physical movements from the user such as jumping, turning etc. The experimental results show that the proposed mobile exergames helps its users to burn calories and have fun at the same time.

关键词： accelerometers cameras computer games gyroscopes health care mobile computing object detection object tracking smart phones

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Unsupervised Domain Adaptation for Cardiac Segmentation: Towards Structure Mutual Information Maximization

Unsupervised Domain Adaptation for Cardiac Segmentation: Tow...

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

作者： Lu, Changjie Zheng, Shen Gupta, Gaurav Wenzhou Kean Univ Coll Sci & Technol Wenzhou Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Unsupervised domain adaptation approaches have recently succeeded in various medical image segmentation tasks. The reported works often tackle the domain shift problem by aligning the domain-invariant features and minimizing the domain-specific discrepancies. That strategy works well when the difference between a specific domain and between different domains is slight. However, the generalization ability of these models on diverse imaging modalities remains a significant challenge. This paper introduces UDA-VAE++, an unsupervised domain adaptation framework for cardiac segmentation with a compact loss function lower bound. To estimate this new lower bound, we develop a novel Structure Mutual Information Estimation (SMIE) block with a global estimator, a local estimator, and a prior information matching estimator to maximize the mutual information between the reconstruction and segmentation tasks. Specifically, we design a novel sequential reparameterization scheme that enables information flow and variance correction from the low-resolution latent space to the high-resolution latent space. Comprehensive experiments on benchmark cardiac segmentation datasets demonstrate that our model outperforms previous state-of-the-art qualitatively and quantitatively.

关键词： Image segmentation Adaptation models conferences Estimation Benchmark testing pattern recognition Task analysis

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Overparametrization of HyperNetworks at Fixed FLOP-Count Enables Fast Neural Image Enhancement

Overparametrization of HyperNetworks at Fixed FLOP-Count Ena...

引用

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

作者： Muller, Lorenz K. Huawei Technol Zurich Res Ctr Zurich Switzerland

ISBN: (纸本)9781665448994

Deep convolutional neural networks can enhance images taken with small mobile camera sensors and excel at tasks like demoisaicing, denoising and super-resolution. However, for practical use on mobile devices these networks often require too many FLOPs and reducing the FLOPs of a convolution layer, also reduces its parameter count. This is problematic in view of the recent finding that heavily over-parameterized neural networks are often the ones that generalize best. In this paper we propose to use HyperNetworks to break the fixed ratio of FLOPs to parameters of standard convolutions. This allows us to exceed previous state-of-the-art architectures in SSIM and MS-SSIM on the Zurich RAWto-DSLR (ZRR) data-set at > 10x reduced FLOP-count. On ZRR we further observe generalization curves consistent with 'double-descent' behavior at fixed FLOP-count, in the large image limit. Finally we demonstrate the same technique can be applied to an existing network (VDN) to reduce its computational cost while maintaining fidelity on the Smartphone Image Denoising Dataset (SIDD). Code for key functions is given in the supplemental.

关键词： Training Convolution Noise reduction computer architecture Cameras Energy efficiency Sensors

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CSG0: Continual Urban Scene Generation with Zero Forgetting

CSG0: Continual Urban Scene Generation with Zero Forgetting

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

作者： Jain, Himalaya Tuan-Hung Vu Perez, Patrick Cord, Matthieu Valeo Ai Paris France Sorbonne Univ Paris France

ISBN: (纸本)9781665487399

With the rapid advances in generative adversarial networks (GANs), the visual quality of synthesised scenes keeps improving, including for complex urban scenes with applications to automated driving. We address in this work a continual scene generation setup in which GANs are trained on a stream of distinct domains;ideally, the learned models should eventually be able to generate new scenes in all seen domains. This setup reflects the real-life scenario where data are continuously acquired in different places at different times. In such a continual setup, we aim for learning with zero forgetting, i.e., with no degradation in synthesis quality over earlier domains due to catastrophic forgetting. To this end, we introduce a novel framework that not only (i) enables seamless knowledge transfer in continual training but also (ii) guarantees zero forgetting with a small overhead cost. While being more memory efficient, thanks to continual learning, our model obtains better synthesis quality as compared against the brute-force solution that trains one full model for each domain. Especially, under extreme low-data regimes, our approach outperforms the brute-force one by a large margin.

关键词： Training Visualization Costs Semantics Memory management Generative adversarial networks pattern recognition

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It's Written All Over Your Face: Full-Face Appearance-Based Gaze Estimation 30

It's Written All Over Your Face: Full-Face Appearance-Based ...

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30th IEEE/CVF conference on computer vision and pattern recognition workshops (CVPRW)

作者： Zhang, Xucong Sugano, Yusuke Fritz, Mario Bulling, Andreas Max Planck Inst Informat Perceptual User Interfaces Grp Saarland Informat Campus Saarbrucken Germany Max Planck Inst Informat Scalable Learning & Percept Grp Saarland Informat Campus Saarbrucken Germany Osaka Univ Grad Sch Informat Sci & Technol Suita Osaka Japan Max Planck Inst Informat Saarbrucken Germany

ISBN: (纸本)9781538607336

Eye gaze is an important non-verbal cue for human affect analysis. Recent gaze estimation work indicated that information from the full face region can benefit performance. Pushing this idea further, we propose an appearance-based method that, in contrast to a long-standing line of work in computer vision, only takes the full face image as input. Our method encodes the face image using a convolutional neural network with spatial weights applied on the feature maps to flexibly suppress or enhance information in different facial regions. Through extensive evaluation, we show that our full-face method significantly outperforms the state of the art for both 2D and 3D gaze estimation, achieving improvements of up to 14.3% on MPIIGaze and 27.7% on EYEDIAP for person-independent 3D gaze estimation. We further show that this improvement is consistent across different illumination conditions and gaze directions and particularly pronounced for the most challenging extreme head poses.

关键词： computer vision

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A Non-invasive vision Based Approach to Velocity Measurement of Skeleton Training

A Non-invasive Vision Based Approach to Velocity Measurement...

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

作者： Evans, Murray Needham, Laurie Colyer, Steffi L. Cosker, Darren P. Univ Bath Dept Comp Sci Ctr Anal Mot Entertainment Res & Applicat CAMERA Bath Avon England Univ Bath Dept Hlth CAMERA Bath Avon England Univ Bath Dept Comp Sci CAMERA Bath Avon England

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193601

Skeleton is a winter sport where performance is greatly affected by the velocity an athlete can achieve during their start up to the point where they load themselves onto their sled. As such, it is of interest to athletes and coaching staff to be able to monitor the performance of their athletes and how they respond to different training schedules and techniques. This paper proposes a non-invasive vision based method for measuring the velocity of a skeleton athlete and their sled during the push start. Mean differences in estimated velocity between ground truth data and our proposed system were -0.005 (+/- 0.186) m.s(-1) for the athlete mass centre and -0.017 (+/- 0.133) m.s(-1) for the sled. The results compare favourably to techniques previously presented in the biomechanics and sport science literature.

关键词： Cameras Skeleton Image segmentation Head Three-dimensional displays Torso Tracking

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Densely Self-guided Wavelet Network for Image Denoising

Densely Self-guided Wavelet Network for Image Denoising

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

作者： Liu, Wei Yan, Qiong Zhao, Yuzhi SenseTime Res Hong Kong Peoples R China Harbin Inst Technol Harbin Heilongjiang Peoples R China City Univ Hong Kong Hong Kong Peoples R China

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193601

During the past years, deep convolutional neural networks have achieved impressive success in image denoising. In this paper, we propose a densely self-guided wavelet network (DSWN) for real-world image denoising. The basic structure of DSWN is a top-down self-guidance architecture which is able to efficiently incorporate multi- scale information and extract good local features to recover clean images. Moreover, such a structure requires a smaller number of parameters and enables us to achieve better effectiveness than Unet structure. To avoid information loss and achieve a better receptive field size, we embed wavelet transform into DSWN. In addition, we apply densely residual learning to convolution blocks to enhance the feature extraction capability of the proposed network. At the full resolution level of DSWN, we adopt a double branch structure to generate the final output. One branch of them tends to pay attention to dark areas and the other performs better on bright areas. Such a double branch strategy is able to handle the noise at different exposures. The proposed network is validated by BSD68, Kodak24 and SIDD+ benchmark. Additional experimental results show that the proposed network outperforms most state-of-the-art image denoising solutions.

关键词： computer vision pattern recognition conferences

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SrvfNet: A Generative Network for Unsupervised Multiple Diffeomorphic Functional Alignment

SrvfNet: A Generative Network for Unsupervised Multiple Diff...

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

作者： Nunez, Elvis Lizarraga, Andrew Joshi, Shantanu H. Univ Calif Los Angeles Dept Elect & Comp Engn Los Angeles CA 90024 USA Univ Calif Los Angeles Dept Bioengn Los Angeles CA 90024 USA Univ Calif Los Angeles Dept Neurol Ahmanson Lovelace Brain Mapping Ctr Los Angeles CA 90024 USA

ISBN: (纸本)9781665448994

We present SrvfNet, a generative deep learning framework for the joint multiple alignment of large collections of functional data comprising square-root velocity functions (SRVF) to their templates. Our proposed framework is fully unsupervised and is capable of aligning to a predefined template as well as jointly predicting an optimal template from data while simultaneously achieving alignment. Our network is constructed as a generative encoder-decoder architecture comprising fully-connected layers capable of producing a distribution space of the warping functions. We demonstrate the strength of our framework by validating it on synthetic data as well as diffusion profiles from magnetic resonance imaging (MRI) data.

关键词： Deep learning computer vision Magnetic resonance imaging conferences Computational modeling Training data computer architecture

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Multiple Object Detection and Tracking in the Thermal Spectrum

Multiple Object Detection and Tracking in the Thermal Spectr...

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

作者： El Ahmar, Wassim A. Kolhatkar, Dhanvin Nowruzi, Farzan Erlik AlGhamdi, Hamzah Hou, Jonathan Laganiere, Robert Univ Ottawa Ottawa ON Canada Sensor Cortek Inc Ottawa ON Canada Pleora Technol Kanata ON Canada

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Multiple Object Tracking (MOT) is an integral part of machine vision research. Most tracking-by-detection based MOT solutions utilize video streams from RGB cameras for their operation. However, for real-world applications, it is necessary to utilize sensors that operate in different spectrums to accommodate for varying lighting conditions. Since object detection is the first step of the tracking pipeline in tracking-by-detection approaches, we compare the performance of state-of-the-art object detectors when trained on color images to their performance when trained on thermal images. We introduce a new dataset for multiple object tracking with thermal images and corresponding RGB images and show that state-of-the-art trackers perform better on thermal images, especially in poor lighting conditions. Finally, we propose the use of a dynamic cut-off threshold for tracking-by-detection approaches that factors the size of a predicted box to enhance the tracker association. Our dataset and source code are publicly available at https://***/wassimea/thermalMOT

关键词： Machine vision conferences Pipelines Lighting Object detection Detectors Streaming media

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Understanding the Nature of First-Person Videos: Characterization and Classification using Low-Level Features

Understanding the Nature of First-Person Videos: Characteriz...

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27th IEEE conference on computer vision and pattern recognition (CVPR)

作者： Tan, Cheston Goh, Hanlin Chandrasekhar, Vijay Li, Liyuan Lim, Joo-Hwee Inst Infocomm Res Singapore Singapore

ISBN: (纸本)9781479943098

First-person view (FPV) video data is set to proliferate rapidly, due to many consumer wearable-camera devices coming onto the market. Research into FPV (or "egocentric") vision is also becoming more common in the computer vision community. However, it is still unclear what the fundamental characteristics of such data are. How is it really different from third-person view (TPV) data? Can all FPV data be treated the same? In this first attempt to approach these questions in a quantitative and empirical manner, we analyzed a meta-collection of 21 FPV and TPV datasets totaling more than 165 hours of video. We performed the first quantitative characterization of FPV videos over multiple datasets, encompassing virtually all available FPV datasets. Validating this characterization, linear classifiers trained on low-level features to perform FPVversus-TPV classification achieved good baseline performance. Accuracy peaked at 81% for 2-minute clips, but 67% accuracy was achieved even with 1-second clips. Our low-level features are fast to compute and do not require annotation. Overall, our work uncovered insights regarding the basic nature and characteristics of FPV data.

关键词： LOW LEVEL Videotapes Pepper golden mosaic virus Dataset images computer vision clipping Clip Camera device

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