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Smart Traffic Management System using Deep Learning for Smart City Applications 9

Smart Traffic Management System using Deep Learning for Smar...

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9th IEEE Annual Computing and Communication workshop and Conference (CCWC)

作者： Lingani, Guy M. Rawat, Danda B. Garuba, Moses Howard Univ Dept Elect Engn & Comp Sci Washington DC 20059 USA

ISBN: (纸本)9781728105543

Already known as densely populated areas with land use including housing, transportation, sanitation, utilities and communication, nowadays, cities tend to grow even bigger. Genuine road-user's types are emerging with further technological developments to come. As cities population size escalates, and roads getting congested, government agencies such as Department of Transportation (DOT) through the National Highway Traffic Safety Administration (NHTSA) are in pressing need to perfect their management systems with new efficient technologies. The challenge is to anticipate on never before seen problems, in their effort to save lives and implement sustainable cost-effective management systems. To make things yet more complicated and a bit daunting, self-driving car will be authorized in a close future in crowded major cities where roads are to be shared among pedestrians, cyclists, cars, and trucks. Roads sizes and traffic signaling will need to be constantly adapted accordingly. Counting and classifying turning vehicles and pedestrians at an intersection is an exhausting task and despite traffic monitoring systems use, human interaction is heavily required for counting. Our approach to resolve traffic intersection turning-vehicles counting is less invasive, requires no road dig up or costly installation. Live or recorded videos from already installed camera all over the cities can be used as well as any camera including cellphones. Our system is based on Neural Network and Deep Learning of object detection along computer vision technology and several methods and algorithms. Our approach will work on still images, recorded-videos, real-time live videos and will detect, classify, track and compute moving object velocity and direction using convolution neural network. Created based upon series of algorithms modeled after the human brain, our system uses NVIDIA Video cards with GPU, CUDA, OPENCV and mathematical vectors systems to perform.

关键词： Deep Learning computer vision Neural network Convolutional Neural Network Detection Tracking Counting Video GPU Intersection

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Interactive Endoscopy: A Next-Generation, Streamlined user Interface for Lung Surgery Navigation 22nd

Interactive Endoscopy: A Next-Generation, Streamlined User I...

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10th International workshop on Machine Learning in Medical Imaging (MLMI) / 22nd International Conference on Medical Image Computing and computer-Assisted Intervention (MICCAI)

作者： Thienphrapa, Paul Bydlon, Torre Chen, Alvin Vagdargi, Prasad Varble, Nicole Stanton, Douglas Popovic, Aleksandra Philips Res North Amer Cambridge MA 02141 USA Johns Hopkins Univ I STAR Lab Baltimore MD USA

ISBN: (纸本)9783030322540;9783030322533

computer generated graphics are superimposed onto live video emanating from an endoscope, offering the surgeon visual information that is hiding in the native scene-this describes the classical scenario of augmented reality in minimally invasive surgery. Research efforts have, over the past few decades, pressed considerably against the challenges of infusing a priori knowledge into endoscopic streams. As framed, these contributions emulate perception at the level of the surgeon expert, perpetuating debates on the technical, clinical, and societal viability of the proposition. We herein introduce interactive endoscopy, transforming passive visualization into an interface that allows the surgeon to label noteworthy anatomical features found in the endoscopic video, and have the virtual annotations remember their tissue locations during surgical manipulation. The streamlined interface combines vision-based tool tracking and speech recognition to enable interactive selection and labeling, followed by tissue tracking and optical flow for label persistence. These discrete capabilities have matured rapidly in recent years, promising technical viability of the system;it can help clinicians offload the cognitive demands of visually deciphering soft tissues;and supports societal viability by engaging, rather than emulating, surgeon expertise. Through a video-assisted thoracotomy use case, we develop a proof-of-concept to improve workflow by tracking surgical tools and visualizing tissue, while serving as a bridge to the classical promise of augmented reality in surgery.

关键词： Interactive endoscopy Lung surgery VATS Augmented reality human-computer interaction

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“All Right, Mr. DeMille, I’m Ready for My Closeup:” Adding Meaning to user Actions from Video for Immersive Analytics

“All Right, Mr. DeMille, I’m Ready for My Closeup:” Addin...

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Machine Learning from user interaction for Visualization and Analytics (MLUI), IEEE workshop on

作者： Andrea Batch Niklas Elmqvist College of Information Studies University of Maryland College Park MD USA

While the use of machine learning and computer vision to classify human behavior has grown into a large, well-established, interdisciplinary area of research, one area that is somewhat overlooked is the intersection of computer vision as a tool for evaluating user behavior in Virtual Reality, particularly in the context of immersive analytics and visualization. We draw on the literature from pattern recognition, computer vision, and machine learning to compose a simple, comparatively resource-cheap pipeline for camera-based extraction of features of professional analyst users and of their sessions in an existing VR visualization system, ImAxes. Our results show high accuracy in predicting self-reported features of the users, even as survey responses about user experience with the immersive interface are somewhat ambiguous in varying based on these features.

关键词： human computer interaction computer vision Visual analytics Design methodology Pipelines Virtual reality Feature extraction

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EyeWear 2018: Second workshop on EyeWear Computing 18

EyeWear 2018: Second Workshop on EyeWear Computing

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ACM International Joint Conference on Pervasive and Ubiquitous Computing / ACM International Symposium on Wearable computers (UbiComp/ISWC)

作者： Tag, Benjamin Augereau, Olivier Holz, Christian Uema, Yuji Lukowicz, Paul Kunze, Kai Keio Univ Yokohama Kanagawa Japan Osaka Prefecture Univ Osaka Japan Microsoft Res Redmond WA USA JINS Inc Tokyo Japan DFKI Kaiserslautern Germany

ISBN: (纸本)9781450359665

Virtual/augmented reality headsets, smart sensing glasses and similar "Smart Eyewear" have recently emerged as commercial products and can provide an interesting research platform for a range of research fields, including human-computer interaction, ubiquitous computing, pervasive sensing, and social sciences. The proposed workshop will bring together researchers from a wide range of computing disciplines, such as mobile and ubiquitous computing, eye tracking, optics, computer vision, human vision and perception, usability, as well as systems research. The workshop is a continuation from 2016 and will focus on discussing application scenarios and shared interest use cases for eyewear computing between corporate and academic researchers.

关键词： Eyewear Head-mounted Displays Smart Glasses Virtual Reality Augmented Reality Activity Recognition Perception-Aware Computing human Augmentation

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Talking With Your Hands: Scaling Hand Gestures and Recognition With CNNs

Talking With Your Hands: Scaling Hand Gestures and Recogniti...

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International Conference on computer vision workshops (ICCV workshops)

作者： Okan Köpüklü Yao Rong Gerhard Rigoll Technical University of Munich Munich Germany Institute for Human-Machine Communication TU Munich Germany Infineon Technologies AG Germany

ISBN: (数字)9781728150239

ISBN: (纸本)9781728150246

The use of hand gestures provides a natural alternative to cumbersome interface devices for human-computer interaction (HCI) systems. As the technology advances and communication between humans and machines becomes more complex, HCI systems should also be scaled accordingly in order to accommodate the introduced complexities. In this paper, we propose a methodology to scale hand gestures by forming them with predefined gesture-phonemes, and a convolutional neural network (CNN) based framework to recognize hand gestures by learning only their constituents of gesture-phonemes. The total number of possible hand gestures can be increased exponentially by increasing the number of used gesture-phonemes. For this objective, we introduce a new benchmark dataset named Scaled Hand Gestures Dataset (SHGD) with only gesture-phonemes in its training set and 3-tuples gestures in the test set. In our experimental analysis, we achieve to recognize hand gestures containing one and three gesture-phonemes with an accuracy of 98.47% (in 15 classes) and 94.69% (in 810 classes), respectively. Our dataset, code and pretrained models are publicly available.

关键词： human computer interaction Three-dimensional displays Training Two dimensional displays Decoding Gesture recognition Task analysis

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Improving marker-based tracking for augmented reality in underwater environments

Improving marker-based tracking for augmented reality in und...

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2018 Eurographics workshop on Graphics and Cultural Heritage, GCH 2018

作者： Cejka, Jan Marek, U. Agrafiotis, Panagiotis Skarlatos, Dimitrios Bruno, Fabio Liarokapis, Fotis Human Computer Interaction Laboratory Faculty of Informatics Masaryk University Czech Republic Photogrammetric Vision Laboratory Department of Civil Engineering and Geomatics Cyprus University of Technology Cyprus Cyprus Research S.r.l. - University of Calabria Italy

ISBN: (纸本)9783038680574

Underwater sites are a challenging environment for augmented reality. Images taken under water are degraded in several ways, most importantly they are affected by unbalanced colors due to uneven absorption of light in each color channel, blurring and desaturation caused by turbidity, or noise due to the presence of larger organisms, plants, or bubbles. In this paper, we introduce a new method based on white balancing that enhances underwater images to improve the results of detection of markers. We compare this method with several image enhancement methods, and evaluate their performance when applied to the problem of detecting markers under water. Our results show that our method improves the detection in underwater environments while keeping the computation time low. © 2018 GCH 2018 - Eurographics workshop on Graphics and Cultural Heritage. All rights reserved.

关键词： Augmented reality

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SIMULATION OF THE "COSMONAUT-ROBOT" SYSTEM interaction ON THE LUNAR SURFACE BASED ON METHODS OF MACHINE vision AND computer GRAPHICS 2

SIMULATION OF THE "COSMONAUT-ROBOT" SYSTEM INTERACTION ON TH...

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International workshop on Photogrammetric and computer vision Techniques for Video Surveillance, Biometrics and Biomedicine

作者： Kryuchkov, B. I. Usov, V. M. ' Chertopolokhov, V. A. Ronzhin, A. L. Karpov, A. A. Yu Gagarin Res & Training Cosmonauts Ctr Star City Moscow Region Russia Lomonosov Moscow State Univ Math Mech Fac Moscow Russia Russian Acad Sci St Petersburg Inst Informat & Automat St Petersburg Russia

Extravehicular activity (EVA) on the lunar surface, necessary for the future exploration of the Moon, involves extensive use of robots. One of the factors of safe EVA is a proper interaction between cosmonauts and robots in extreme environments. This requires a simple and natural man-machine interface, e.g. multimodal contactless interface based on recognition of gestures and cosmonaut's poses. When travelling in the "Follow Me" mode (master/slave), a robot uses onboard tools for tracking cosmonaut's position and movements, and on the basis of these data builds its itinerary. The interaction in the system "cosmonaut-robot" on the lunar surface is significantly different from that on the Earth surface. For example, a man, dressed in a space suit, has limited fine motor skills. In addition, EVA is quite tiring for the cosmonauts, and a tired human being less accurately performs movements and often makes mistakes. All this leads to new requirements for the convenient use of the man-machine interface designed for EVA. To improve the reliability and stability of human-robot communication it is necessary to provide options for duplicating commands at the task stages and gesture recognition. New tools and techniques for space missions must be examined at the first stage of works in laboratory conditions, and then in field tests (proof tests at the site of application). The article analyzes the methods of detection and tracking of movements and gesture recognition of the cosmonaut during EVA, which can be used for the design of human-machine interface. A scenario for testing these methods by constructing a virtual environment simulating EVA on the lunar surface is proposed. Simulation involves environment visualization and modeling of the use of the "vision" of the robot to track a moving cosmonaut dressed in a spacesuit.

关键词： Lunar Exploration Extravehicular Activity (EVA) human-Robot interaction (HRI) Mobile Robot Control "Follow Me" Mode Gesture Interface Object Tracking Gesture Recognition Motion Capture

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workshop on Object Recognition for Input and Mobile interaction 17

Workshop on Object Recognition for Input and Mobile Interact...

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19th International Conference on human-computer interaction with Mobile Devices and Services (MobileHCI)

作者： Yeo, Hui-Shyong Gillian, Nicholas Laput, Gierad Quigley, Aaron Univ St Andrews Sch Comp Sci St Andrews Fife Scotland Google ATAP Mountain View CA USA Carnegie Mellon Univ Sch Comp Sci Pittsburgh PA 15213 USA

ISBN: (纸本)9781450350754

Today we can see an increasing number of object recognition systems of very different sizes, portability, embedability and form factors which are starting to become part of the ubiquitous, tangible, mobile and wearable computing ecosystems that we might make use of in our daily lives. These systems rely on a variety of technologies including computer vision, radar, acoustic sensing, tagging and smart objects. Such systems open up a wide-range of new forms of touch-less and mobile interaction. With systems deployed in mobile products then using everyday objects that can be found in the office or home, we can realise new applications and novel types of interaction. Object based interactions might revolutionise how people interact with a computer. System could be used in conjunction with a mobile phone, for example it could be trained to open a recipe app when you hold a phone to your stomach, or change its settings when operating with a gloved hand. Although the last few years have seen an increasing amount of research in this area, knowledge about this subject remains under explored, fragmented, and cuts across a set of related but heterogeneous issues. This workshop brings together researchers and practitioners interested in the challenges posed by Object Recognition for Input and Mobile interaction.

关键词： Radar Context-Aware interaction Material Classification Object Recognition Ubiquitous Computing interaction Techniques and Mobile Computing

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Investigation of human-Robot Comfort with a small Unmanned Aerial Vehicle compared to a Ground Robot

Investigation of Human-Robot Comfort with a small Unmanned A...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) / workshop on Machine Learning Methods for High-Level Cognitive Capabilities in Robotics

作者： Acharya, Urja Bevins, Alisha Duncan, Brittany A. Univ Nebraska Dept Comp Sci & Engn Lincoln NE 68588 USA

ISBN: (纸本)9781538626825

This paper presents an investigation of human comfort with a small Unmanned Aerial Vehicle (sUAV) through a study offering a comparison of comfort with a sUAV versus a ground vehicle. Current research on human comfort with sUAVs has been limited to a single previous study, which did not include free flight, and while ground vehicle distancing has been studied, it has never been directly compared to a sUAV. The novelty in the approach is the use of a motion capture room to achieve smooth trajectories and precise measurements, while conducting the first free flight study to compare human comfort after interaction with aerial versus ground vehicles (within subjects, N = 16). These results will contribute to understanding of social, collaborative, and assistive robots, with implications for general human-robot interactions as they evolve to include aerial vehicles. Based on the reduced stress and distance (36.5 cm or 1.2ft) for ground vehicles and increased stress and distance (65.5cm or 2.15ft) for sUAVs, it is recommended that studies be conducted to understand the implications of design features on comfort in interactions with sUAVs and how they differ from those with ground robots.

关键词： Cameras Robot vision systems Land vehicles Stress Unmanned aerial vehicles Aerospace electronics

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Extracting emotions from speech using a bag-of-visual-words approach

Extracting emotions from speech using a bag-of-visual-words ...

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International workshop on Semantic Media Adaptation and Personalization (SMAP)

作者： Evaggelos Spyrou Theodoros Giannakopoulos Dimitrios Sgouropoulos Michalis Papakostas Institute of Informatics and Telecommunications National Center for Scientific Research – “Demokritos” Athens Greece Computer Science and Eng. Department University of Texas at Arlington Arlington USA

ISBN: (纸本)9781538607572

Recognition of humans' emotions may be crucial in certain applications involving e.g., human-computer interaction, monitoring of elderly, understanding the affective state of learners during a course etc. To this goal and depending on the application and the environment, one may use physiological parameters (e.g., heart rate, brain activity etc.) which are typically obtrusive, or analyze other modalities that may be extracted by simply observing a human, such as visual (e.g., her/his facial expressions, gestures, skeletal motion etc.) or audio (e.g., speech). In many applications the only available modality is the latter one, i.e., the human's voice. In this work we aim to analyze a speaker's emotions by relying only on paralinguistic information, extracted by her/his voice, thus discarding the linguistic aspect of speech (i.e., the spoken words). To this goal, we propose a novel emotion classification approach that has been inspired by computer vision tasks. We use a spectrogram, which is a visual representation of the spectrum of an audio segment. We then extract features and code them using a visual vocabulary and represent a spectrogram as a “bag-of-visual words.” This representation is used for classifying an audio segment to an emotion class. We evaluate our approach on 3 datasets that contain speech from different languages and compare it to baseline methods.

关键词： Visualization Feature extraction Speech Spectrogram Vocabulary Emotion recognition computer vision

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