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Detection of Distracted Driver using Convolutional Neural Network 31

Detection of Distracted Driver using Convolutional Neural Ne...

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

作者： Baheti, Bhakti Gajre, Suhas Talbar, Sanjay SGGS Inst Engn & Technol Ctr Excellence Signal & Image Proc Nanded Maharashtra India

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Number of road accidents is continuously increasing in last few years worldwide. As per the survey of National Highway Traffic Safety Administrator, nearly one in five motor vehicle crashes are caused by distracted driver. We attempt to develop an accurate and robust system for detecting distracted driver and warn him against it. Motivated by the performance of Convolutional Neural Networks in computer vision, we present a CNN based system that not only detects the distracted driver but also identifies the cause of distraction. VGG-16 architecture is modified for this particular task and various regularization techniques are implied in order to improve the performance. Experimental results show that our system outperforms earlier methods in literature achieving an accuracy of 96.31% and processes 42 images per second on GPU. We also study the effect of dropout, L2 regularization and batch normalisation on the performance of the system. Next, we present a modified version of our architecture that achieves 95.54% classification accuracy with the number of parameters reduced from 140M in original VGG-16 to 15M only.

关键词： Vehicles computer architecture Vehicle crash testing Convolutional neural networks Task analysis Wheels Roads

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

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

引用

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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Autonomous detection of disruptions in the intensive care unit using deep mask R-CNN 31

Autonomous detection of disruptions in the intensive care un...

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

作者： Malhotra, Kumar Rohit Davoudi, Anis Siegel, Scott Bihorac, Azra Rashidi, Parisa Univ Florida Gainesville FL 32611 USA

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Patients staying in the Intensive Care Unit (ICU) have a severely disrupted circadian rhythm. Due to patients' critical medical condition, ICU physicians and nurses have to provide round-the-clock clinical care, further disrupting patients' circadian rhythm. Mistimed family visits during rest-time can also disrupt patients' circadian rhythm. Currently, such effects are only reported based on hospital visitation policies rather than the actual number of visitors and care providers in the room. To quantify visitation disruptions, we used a deep Mask R-CNN model, a deep learning framework for object instance segmentation to detect and quantify the number of individuals in the ICU unit. This study represents the first effort to automatically quantify visitations in an ICU room, which could have implications in terms of policy adjustment, as well as circadian rhythm investigation. Our model achieved precision of 0.97 and recall of 0.67, with F1 score of 0.79 for detecting disruptions in the ICU units.

关键词： Hospitals Circadian rhythm Biomedical imaging computer vision Timing Lighting conferences

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Onboard Stereo vision for Drone Pursuit or Sense and Avoid 31

Onboard Stereo Vision for Drone Pursuit or Sense and Avoid

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

作者： Cigla, Cevahir Thakker, Rohan Matthies, Larry Aselsan Inc Yenimahalle Ankara Turkey CALTECH Jet Prop Lab Pasadena CA 91125 USA

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

We describe a new, on-board, short range perception system that enables micro aerial vehicles (MAVs) to detect, track, and follow or avoid nearby drones (within 2-20 meters) in GPS-denied environments. Each vehicle is able to sense its neighborhood and adapt its motion accordingly without use of centralized reasoning or inter-vehicle communication. To enable a lightweight, low power solution, on-board stereo cameras are used for detection and tracking with depth images, while a downward-looking camera and an inertial measurement unit are used to estimate the position of the observer without use of GPS. We illustrate the robustness and accuracy of this approach through real-time, outdoor leader-follower experiments with three quadrotors. Our experiments show that state-of-art trackers are far less robust in detection against cluttered background. This demonstrates that stereo vision is a highly effective approach to perception for safe navigation of multiple MAVs in close proximity.

关键词： Radar tracking Target tracking Drones Cameras Three-dimensional displays Observers

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IR2VI: Enhanced Night Environmental Perception by Unsupervised Thermal Image Translation 31

IR2VI: Enhanced Night Environmental Perception by Unsupervis...

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

作者： Liu, Shuo John, Vijay Blasch, Erik Liu, Zheng Huang, Ying Univ British Columbia Vancouver BC Canada Toyota Inst Technol Nagoya Aichi Japan Air Force Res Lab Wright Patterson AFB OH USA Chongqing Univ Posts & Telecommun Chongqing Peoples R China

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Context enhancement is critical for night vision (NV) applications, especially for the dark night situation without any artificial lights. In this paper, we present the infrared-to-visual (IR2VI) algorithm, a novel unsupervised thermal-to-visible image translation framework based on generative adversarial networks (GANs). IR2VI is able to learn the intrinsic characteristics from VI images and integrate them into IR images. Since the existing unsupervised GAN-based image translation approaches face several challenges, such as incorrect mapping and lack of fine details, we propose a structure connection module and a region-of-interest (ROI) focal loss method to address the current limitations. Experimental results show the superiority of the IR2VI algorithm over baseline methods.

关键词： Training Gallium nitride Image fusion Generators Night vision Task analysis Semantics

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GenLR-Net: Deep framework for very low resolution face and object recognition with generalization to unseen categories 31

GenLR-Net: Deep framework for very low resolution face and o...

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

作者： Mudunuri, Sivaram Prasad Sanyal, Soubhik Biswas, Soma Indian Inst Sci Dept Elect Engn Bangalore Karnataka India Max Planck Inst Intelligent Syst Perceiving Syst Dept Tubingen Germany

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Matching very low resolution images of faces and objects with high resolution images in the database has important applications in surveillance scenarios, street-to-shop matching for general objects, etc. Matching across huge resolution difference along with variations in illumination, view-point, etc. makes the problem quite challenging. The problem becomes even more difficult if the testing objects have not been seen during training. In this work, we propose a novel deep convolutional neural network architecture to address these problems. We systematically introduce different kinds of constraints at different stages of the architecture so that the approach can recognize low resolution images as well as generalize well to images of unseen categories. The reason behind each additional step along with its effect on the overall performance is thoroughly analyzed. Extensive experiments are conducted on two face and object datasets which justifies the effectiveness of the proposed approach for handling these real-life challenging scenarios.

关键词： Image resolution Face Training Object recognition Databases Testing Task analysis

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Learning Fashion By Simulated Human Supervision 31

Learning Fashion By Simulated Human Supervision

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

作者： Alshan, Eli Alpert, Sharon Neuberger, Assaf Bubis, Nathaniel Oks, Eduard Amazon Lab126 Sunnyvale CA 94089 USA

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

We consider the task of predicting subjective fashion traits from images using neural networks. Specifically, we are interested in training a network for ranking outfits according to how well they fit the user. In order to capture the variability induced by human subjective considerations, each training example is annotated by a panel of fashion experts. Similarly to previous works on subjective data, the panel votes are converted to a classification or regression problem and the corresponding network is trained and evaluated using standard objective metrics. The question is which objective metric, if any, is most suitable to measure the performance of a network trained for subjective tasks? In this paper, we conducted human approval tests for outfit ranking networks trained using various objective metrics. We show that these metrics do not adequately estimate the human approval of subjective tasks. Instead, we introduce a supervising network that unlike objective metrics, is designed to capture the variability induced by human subjectivity. We use it to supervise our outfit ranking network and we demonstrate empirically, that training our outfit ranking network with the suggested supervising network achieves greater approval ratings from human subjects.

关键词： Task analysis Training Measurement Level set Prediction algorithms computer vision Standards

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A Deep Face Identification Network Enhanced by Facial Attributes Prediction 31

A Deep Face Identification Network Enhanced by Facial Attrib...

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

作者： Taherkhani, Fariborz Nasrabadi, Nasser M. Dawson, Jeremy West Virginia Univ Morgantown WV 26506 USA

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

In this paper, we propose a new deep framework which predicts facial attributes and leverage it as a soft modality to improve face identification performance. Our model is an end to end framework which consists of a convolutional neural network (CNN) whose output is fanned out into two separate branches;the first branch predicts facial attributes while the second branch identifies face images. Contrary to the existing multi-task methods which only use a shared CNN feature space to train these two tasks jointly, we fuse the predicted attributes with the features from the face modality in order to improve the face identification performance. Experimental results show that our model brings benefits to both face identification as well as facial attribute prediction performance, especially in the case of identity facial attributes such as gender prediction. We tested our model on two standard datasets annotated by identities and face attributes. Experimental results indicate that the proposed model outperforms most of the current existing face identification and attribute prediction methods.

关键词： Face Facial features Task analysis Predictive models Face recognition Feature extraction Training

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Cosmetic Features Extraction by a Single Image Makeup Decomposition 31

Cosmetic Features Extraction by a Single Image Makeup Decomp...

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

作者： Yamagishi, Kanami Yamamoto, Shintaro Kato, Takuya Morishima, Shigeo Waseda Univ Tokyo Japan Waseda Res Inst Sci & Engn Tokyo Japan

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

In recent years, a large number of makeup images have been shared on social media. Most of these images lack information about the cosmetics used, such as color, glitter or etc., while they are difficult to infer due to the diversity of skin color or lighting conditions. In this paper, our goal is to estimate cosmetic features only from a single makeup image. Previous work has measured the material parameters of cosmetic products from pairs of images showing the face with and without makeup, but such comparison images are not always available. Furthermore, this method cannot represent local effects such as pearl or glitter since they adapted physically-based reflectance models. We propose a novel image-based method to extract cosmetic features considering both color and local effects by decomposing the target image into makeup and skin color using Difference of Gaussian (DoG). Our method can be applied for single, standalone makeup images, and considers both local effects and color. In addition, our method is robust to the skin color difference due to the decomposition separating makeup from skin. The experimental results demonstrate that our method is more robust to skin color difference and captures characteristics of each cosmetic product.

关键词： Image color analysis Feature extraction Dogs Skin Lighting computer vision pattern recognition

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Part-based Player Identification using Deep Convolutional Representation and Multi-scale Pooling 31

Part-based Player Identification using Deep Convolutional Re...

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

作者： Senocak, Arda Oh, Tae-Hyun Kim, Junsik Kweon, In So Korea Adv Inst Sci & Technol Dept EE Daejeon South Korea MIT CSAIL Cambridge MA USA

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

This paper addresses the problem of automatic player identification in broadcast sports videos filmed with a single side-view medium distance camera. Player identification in this setting is a challenging task because visual cues such as faces and jersey numbers are not clearly visible. Thus, this task requires sophisticated approaches to capture distinctive features from players to distinguish them. To this end, we use Convolutional Neural Networks (CNN) features extracted at multiple scales and encode them with an advanced pooling, called Fisher vector. We leverage it for exploring representations that have sufficient discriminatory power and ability to magnify subtle differences. We also analyze the distinguishing parts of the players and present a part based pooling approach to use these distinctive feature points. The resulting player representation is able to identify players even in difficult scenes. It achieves state-of-the-art results up to 96% on NBA basketball clips.

关键词： Videos Cameras Task analysis Visualization Training Sports Face recognition

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