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Eyewear 2021 The Forth workshop on Eyewear Computing - Augmenting Social Situations and Democratizing Tools 21

Eyewear 2021 The Forth Workshop on Eyewear Computing - Augme...

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

作者： Ragozin, Kirill Kunze, Kai Hirzle, Teresa Tag, Benjamin Uema, Yuji Rukzio, Enrico Ward, Jamie A. Keio Univ Yokohama Kanagawa Japan Ulm Univ Ulm Germany Univ Melbourne Melbourne Vic Australia J NS Inc Yokohama Kanagawa Japan Goldsmiths Univ London London England

ISBN: (纸本)9781450384612

Head-worn sensing, especially embedded in augmented and virtual reality (AR/VR) head-mounted displays and smart glasses is currently increasingly moving away from niche applications and small-scale research prototypes to large-scale consumer adoption (e.g. Oculus Quest 2, Hololens 2, J!NS MEME, Bose Frames). Significant progress in sensing technologies and modalities have lead to a constant increase of commercially available products and unobtrusive, affordable research prototypes. These recent advances allow us to extend the Eyewear Community to enable large scale in-situ studies, as one of the favored research methodologies in Ubiquitous Computing. One manifestation of this can already be observed in large scale dataset recording, eyewear student competitions and programming seminars. In this workshop we focus on supporting these large-scale uses of eyewear computing, discussing lessons learned from early deployment and how to empower the community with better hardware/software prototyping tools as well as the establishment of open data sets. In addition, we will discuss long-term psychological and physical impacts and risks of the technology that become increasingly important with a wider distribution of devices to consumers. The proposed workshop will bring together researchers from a wide range of disciplines, such as mobile and ubiquitous computing, activity recognition eye tracking, optics, human vision and perception, usability, as well as systems engineering research. This year it will also bring in researchers from neuroscience, psychology and other fields that might want to apply or use the research systems. The workshop is a continuation from 2016/2018/2019 and will focus on discussing on how to democratizing tools for researchers who want to apply eyewear computing (sensing/interaction) in their fields, yet are no wearable computing experts or computer scientists.

关键词： datasets eyewear computing interactions activity recognition

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Challenges and Opportunities for Designing with Biodata as Material 20

Challenges and Opportunities for Designing with Biodata as M...

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11th Nordic Conference on human-computer interaction (NordiCHI)

作者： Tsaknaki, Vasiliki Jenkins, Tom Boer, Laurens Homewood, Sarah Howell, Noura Sanches, Pedro IT Univ Copenhagen Digital Design Dept Copenhagen Denmark IT Univ Copenhagen IxD Lab Copenhagen Denmark North Carolina State Univ Dept Commun Raleigh NC USA KTH Royal Inst Technol Stockholm Sweden

ISBN: (纸本)9781450375795

This one-day workshop will bring together researchers working with biodata and design for discussing and reflecting upon how design and HCI operate with the body and its data. The main workshop objective will be to identify core challenges and opportunities in this domain. We intend to cohere the projects of the workshop participants into a set of artifacts, images, and theory to create an annotated portfolio that represents the state-of-the art in designing with biodata in HCI. The workshop will be structured around four themes relating to the use and contexts of working with biodata: 1. Making sense of biodata by taking into account a holistic perspective of body, mind and emotions, as exemplified through soma design methods in HCI, for example;2. Feminist perspectives of biodata that take into account bodily transitions, pluralities of bodies and temporalities of bodies;3. Non-anthropocentric perspectives of working with and making sense of biodata, to also account for microorganisms and other material agencies in our bodies;and 4. Emerging domains, contexts, and practices around the use of biodata, such as health, affective, and performance contexts. The significance of the workshop is twofold: One, we will produce a group of researchers and practitioners that are working with biodata, aiming to unpack, discuss and respond to critical issues around designing with and for it. Two, we will create an annotated portfolio that can travel on its own as documentation and vision for future biodata-based design practices.

关键词： biodata interaction design biosensing materials

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human-Object interaction Detection: A Quick Survey and Examination of Methods 1

Human-Object Interaction Detection: A Quick Survey and Exami...

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1st International workshop on human-Centric Multimedia Analysis, HuMA 2020

作者： Bergstrom, Trevor Shi, Humphrey University of Oregon EugeneOR United States

ISBN: (纸本)9781450381512

human-object interaction detection is a relatively new task in the world of computer vision and visual semantic information extraction. With the goal of machines identifying interactions that humans perform on objects, there are many real-world use cases for the research in this field. To our knowledge, this is the first general survey of the state-of-the-art and milestone works in this field. We provide a basic survey of the developments in the field of human-object interaction detection. Many works in this field use multi-stream convolutional neural network architectures, which combine features from multiple sources in the input image. Most commonly these are the humans and objects in question, as well as the spatial quality of the two. As far as we are aware, there have not been in-depth studies performed that look into the performance of each component individually. In order to provide insight to future researchers, we perform an individualized-study that examines the performance of each component of a multi-stream convolutional neural network architecture for human-object interaction detection. Specifically, we examine the HORCNN architecture as it is a foundational work in the field. In addition, we provide an in-depth look at the HICO-DET dataset, a popular benchmark in the field of human-object interaction detection. © 2020 ACM.

关键词： Surveys

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A Routing-based Strategy to Socially Approach Multiple Individuals in Cluttered Environments

A Routing-based Strategy to Socially Approach Multiple Indiv...

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IEEE Latin American Robotics Symposium, LARS

作者： Aline F. F. Silva Douglas G. Macharet Department of Computer Science Computer Vision and Robotics Laboratory (VeRLab) Universidade Federal de Minas Gerais Brazil Laboratório de Inteligência Computacional e Robótica (LICRo) Instituto Federal do Triângulo Mineiro (IFTM) Campus Patrocínio Brazil

ISBN: (数字)9781665462808

ISBN: (纸本)9781665462815

The use of autonomous mobile robots is expected to continue increasing in different parts of society in the next years. In this context, when not restricted to industrial environments only, it is essential to provide such devices with the ability to behave in a so-called socially acceptable manner. In the human-Robot interaction (HRI) field, a fundamental problem is to approach people, i.e., try to initiate and maintain an interaction. This task involves different challenges since some implicit nonverbal social rules must be respected, especially those concerning people's personal spaces (called proxemics). Considering the presence of multiple individuals to be approached, a combinatorial optimization aspect must also be tackled, where an optimal sequence of visits must be selected. In this paper, we formulate this problem as a Generalized Travelling Salesman Problem (GTSP) and combine it with a Probabilistic Roadmap (PRM) planner in order to account for obstacles in the environment. Numerical simulations show that the methodology produces length-optimized paths that are feasible, safe, and respect the social conventions.

关键词： Conferences Education human-robot interaction Probabilistic logic Numerical simulation Space exploration Mobile robots

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Research methods in the field of computer visualization based on virtual reality 30

Research methods in the field of computer visualization base...

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30th International Conference on computer Graphics and Machine vision, GraphiCon 2020

作者： Averbukh, Natalya Averbukh, Vladimir Ural Federal University Yekaterinburg Russia Krasovskii Institute of Mathematics and Mechanics Yekaterinburg Russia

The paper examines research methods in the field of computer visualization based on virtual reality. Developing such systems involves not only the issues of software implementation, modelling or adequate equipment choice, but also the tasks connected with cognitive processes occurring in both users and developers of visualization systems. Featured is a brief review of papers dealing with research in human interaction with virtual reality. The scope of tasks in developing visualization systems based on virtual reality is determined. The conclusion discusses several research issues, the solutions to which will increase the efficiency of interaction of users with virtual environments. © 2020 for this paper by its authors. use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Visualization

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Context-dependent models for predicting and characterizing facial expressiveness 3

Context-dependent models for predicting and characterizing f...

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3rd workshop on Affective Content Analysis, AffCon 2020

作者： Lin, Victoria Girard, Jeffrey M. Morency, Louis-Philippe Carnegie Mellon University PittsburghPA15213 United States

In recent years, extensive research has emerged in affective computing on topics like automatic emotion recognition and determin-ing the signals that characterize individual emotions. Much less stud-ied, however, is expressiveness|the extent to which someone shows any feeling or emotion. Expressiveness is related to personality and men-tal health and plays a crucial role in social interaction. As such, the ability to automatically detect or predict expressiveness can facilitate significant advancements in areas ranging from psychiatric care to artificial social intelligence. Motivated by these potential applications, we present an extension of the BP4D+ dataset [27] with human ratings of expressiveness and develop methods for (1) automatically predicting expressiveness from visual data and (2) defining relationships between interpretable visual signals and expressiveness. In addition, we study the emotional context in which expressiveness occurs and hypothesize that different sets of signals are indicative of expressiveness in different con-texts (e.g., in response to surprise or in response to pain). Analysis of our statistical models confirms our hypothesis. Consequently, by looking at expressiveness separately in distinct emotional contexts, our predictive models show significant improvements over baselines and achieve com-parable results to human performance in terms of correlation with the ground truth. © 2020 CEUR workshop Proceedings.

关键词： computer vision

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Emotion Recognition with Simulated Phosphene vision 2

Emotion Recognition with Simulated Phosphene Vision

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2nd workshop on Multimedia for Accessible human computer Interfaces (MAHCI)

作者： Bollen, Caroline J. M. van Wezel, Richard J. A. van Gerven, Marcel A. J. Gucluturk, Yagmur Radboud Univ Nijmegen Donders Inst Brain Cognit & Behaviour Nijmegen Netherlands

ISBN: (纸本)9781450369121

Electrical stimulation of retina, optic nerve or cortex is found to elicit visual sensations, known as phosphenes. This allows visual prosthetics to partially restore vision by representing the visual field as a phosphene pattern. Since the resolution and performance of visual prostheses are limited, only a fraction of the information in a visual scene can be represented by phosphenes. Here, we propose a simple yet powerful image processing strategy for recognizing facial expressions with prosthetic vision, supporting communication and social interaction in the blind. A psychophysical study was conducted to investigate whether a landmark-based representation of facial expressions could improve emotion detection with prosthetic vision. Our approach was compared to edge detection, which is commonly used in current retinal prosthetic devices. Additionally, the relationship between the number of phosphenes and accuracy of emotion recognition was studied. The landmark model improved accuracy of emotion recognition, regardless of the number of phosphenes. Secondly, the accuracy improved with an increasing number of phosphenes up to a saturation point. The performance saturated with fewer phosphenes with the landmark model than with edge detection. These results suggest that landmark-based image pre-processing allows for a more efficient use of the limited information that can be stored in a phosphene pattern, providing a route towards more meaningful and higher-quality perceptual experience in subjects with prosthetic vision.

关键词： Emotion recognition facial landmark detection visual neuroprosthetics phosphene vision

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Causality Inspired Retrieval of human-object interactions from Video 17

Causality Inspired Retrieval of Human-object Interactions fr...

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17th International Conference on Content-Based Multimedia Indexing (IEEE CBMI)

作者： Zhou, Liting Liu, Jianquan Nishimura, Shoji Antony, Joseph Gurrin, Cathal Dublin City Univ Dublin Ireland NEC Corp Ltd Biometr Res Labs Tokyo Japan

ISBN: (纸本)9781728146737

Notwithstanding recent advances in machine vision, video activity recognition from multiple cameras still remains a challenging task as many real-world interactions cannot be automatically recognised for many reasons, such as partial occlusion or coverage black-spots. In this paper we propose a new technique that infers the unseen relationship between two individuals captured by different cameras and use it to retrieve relevant video clips if there is a likely interaction between the two individuals. We introduce a human object interaction (HOI) model integrating the causal relationship between the humans and the objects. For this we first extract the key frames and generate the labels or annotations using the state-of-the-art image captioning models. Next, we extract SVO (subject, verb, object) triples and encode the descriptions into a vector form for HOI inference using the Stanford CoreNLP parser. In order to calculate the HOI co-existence and the possible causality score we use transfer entropy. From our experimentation, we found that integrating casual relations into the content indexing process and using transfer entropy to calculate the causality score leads to improvement in retrieval performance.

关键词： Cameras Surveillance computer vision Feature extraction Semantics Security Encoding

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Enhancing Process Data in Manual Assembly Workflows 16th

Enhancing Process Data in Manual Assembly Workflows

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14th BPI International workshop / 11th BPM workshop / 1st PODS4H International workshop / 1st AI4BPM International workshop / 1st CCBPM International workshop / 3rd PQ International workshop / 2nd BP-meet-IoT International workshop

作者： Knoch, Soenke Herbig, Nico Ponpathirkoottam, Shreeraman Kosmalla, Felix Staudt, Philipp Fettke, Peter Loos, Peter Saarland Univ German Res Ctr Artificial Intelligence DFKI Saarland Informat Campus Saarbrucken Germany

ISBN: (纸本)9783030116408;9783030116415

The rise of Industry 4.0 and the convergence with BPM provide new potential for the automatic gathering of process-related sensor information. In manufacturing, information about human behavior in manual assembly tasks is rare when no interaction with machines is involved. We suggest technologies to automatically detect material picking and placement in the assembly workflow to gather accurate data about human behavior. For material picking, we use background subtraction;for placement detection image classification with neural networks is applied. The detected fine-grained worker activities are then correlated to a BPMN model of the assembly workflow, enabling the measurement of production time (time per state) and quality (frequency of error) on the shop floor as an entry point for conformance checking and process optimization. The approach has been evaluated in a quantitative case study recording the assembly process 30 times in a laboratory within 4 h. Under these conditions, the classification of assembly states with a neural network provides a test accuracy of 99.25% on 38 possible assembly states. Material picking based on background subtraction has been evaluated in an informal user study with 6 participants performing 16 picks, each providing an accuracy of 99.48%. The suggested method is promising to easily detect fine-grained steps in manufacturing augmenting and checking the assembly workflow.

关键词： Manual assembly computer vision BPM Industry 4.0

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Recognizing the Waving Gesture in the interaction with a Social Robot

Recognizing the Waving Gesture in the Interaction with a Soc...

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IEEE International workshop on Robot and human Communication (ROMAN)

作者： Giovanna Castellano Antonio Cervelione Marco Cianciotta Berardina De Carolis Gennaro Vessio Department of Computer Science University of Bari "Aldo Moro" Bari Italy

ISBN: (数字)9781728160757

ISBN: (纸本)9781728160764

humans use a wide range of non-verbal social signals while communicating with each other. Gestures are part of these signals and social robots should be able to recognize them for responding appropriately during a dialogue and being more socially believable. Gesture recognition is a hot topic in computer vision since a long time. This is particularly due to the fact that the segmentation of foreground objects from a cluttered background is a challenging problem, especially if it has to be performed in real-time. In this paper, we propose a vision-based framework for making social robots capable of recognizing and responding in real-time to a specific greeting gesture, namely the hand waving. The framework is based on a Convolutional Neural Network model trained to recognize hand gestures. Preliminary experiments in a lab setup with the social robot Pepper indicate that the robot correctly recognizes the wave gesture 90% of the times and answers appropriately in real-time by waving itself, thus increasing its social believability.

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