ConfLab: A Rich Multimodal Multisensor Dataset of Free-Standing Social Interactions in the Wild

Recording the dynamics of unscripted human interactions in the wild is challenging due to the delicate trade-offs between several factors: participant privacy, ecological validity, data fidelity, and logistical overheads. To address these, following a 'datasets for the community by the community' ethos, we propose the Conference Living Lab (ConfLab): a new concept for multimodal multisensor data collection of in-the-wild free-standing social conversations. For the first instantiation of ConfLab described here, we organized a real-life professional networking event at a major international conference. Involving 48 conference attendees, the dataset captures a diverse mix of status, acquaintance, and networking motivations. Our capture setup improves upon the data fidelity of prior in-the-wild datasets while retaining privacy sensitivity: 8 videos (1920x1080, 60 fps) from a non-invasive overhead view, and custom wearable sensors with onboard recording of body motion (full 9-axis IMU), privacy-preserving low-frequency audio (1250 Hz), and Bluetooth-based proximity. Additionally, we developed custom solutions for distributed hardware synchronization at acquisition, and time-efficient continuous annotation of body keypoints and actions at high sampling rates. Our benchmarks showcase some of the open research tasks related to in-the-wild privacy-preserving social data analysis: keypoints detection from overhead camera views, skeleton-based no-audio speaker detection, and F-formation detection.Comment: v2 is the version submitted to Neurips 2022 Datasets and Benchmarks Trac

Improved robustness and efficiency for automatic visual site monitoring

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (p. 219-228).Knowing who people are, where they are, what they are doing, and how they interact with other people and things is valuable from commercial, security, and space utilization perspectives. Video sensors backed by computer vision algorithms are a natural way to gather this data. Unfortunately, key technical issues persist in extracting features and models that are simultaneously efficient to compute and robust to issues such as adverse lighting conditions, distracting background motions, appearance changes over time, and occlusions. In this thesis, we present a set of techniques and model enhancements to better handle these problems, focusing on contributions in four areas. First, we improve background subtraction so it can better handle temporally irregular dynamic textures. This allows us to achieve a 5.5% drop in false positive rate on the Wallflower waving trees video. Secondly, we adapt the Dalal and Triggs Histogram of Oriented Gradients pedestrian detector to work on large-scale scenes with dense crowds and harsh lighting conditions: challenges which prevent us from easily using a background subtraction solution. These scenes contain hundreds of simultaneously visible people. To make using the algorithm computationally feasible, we have produced a novel implementation that runs on commodity graphics hardware and is up to 76 faster than our CPU-only implementation. We demonstrate the utility of this detector by modeling scene-level activities with a Hierarchical Dirichlet Process.(cont.) Third, we show how one can improve the quality of pedestrian silhouettes for recognizing individual people. We combine general appearance information from a large population of pedestrians with semi-periodic shape information from individual silhouette sequences. Finally, we show how one can combine a variety of detection and tracking techniques to robustly handle a variety of event detection scenarios such as theft and left-luggage detection. We present the only complete set of results on a standardized collection of very challenging videos.by Gerald Edwin Dalley.Ph.D

A Hierarchical Approach for Associating Body-Worn Sensors to Video Regions in Crowded Mingling Scenarios

We address the complex problem of associating several wearable devices with the spatio-temporal region of their wearers in video during crowded mingling events using only acceleration and proximity. This is a particularly important first step for multi-sensor behavior analysis using video and wearable technologies, where the privacy of the participants must be maintained. Most state-of-the-art works using these two modalities perform their association manually, which becomes practically unfeasible as the number of people in the scene increases. We proposed an automatic association method based on a hierarchical linear assignment optimization, which exploits the spatial context of the scene. Moreover, we present extensive experiments on matching from 2 to more than 69 acceleration and video streams, showing significant improvements over a random baseline in a real world crowded mingling scenario. We also show the effectiveness of our method for incomplete or missing streams (up to a certain limit) and analyze the trade-off between length of the streams and number of participants. Finally, we provide an analysis of failure cases, showing that deep understanding of the social actions within the context of the event is necessary to further improve performance on this intriguing task.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Pattern Recognition and Bioinformatic

A hierarchical approach for associating body-worn sensors to video regions in crowded Mingling scenarios

\u3cp\u3eWe address the complex problem of associating several wearable devices with the spatio-temporal region of their wearers in video during crowded mingling events using only acceleration and proximity. This is a particularly important first step for multisensor behavior analysis using video and wearable technologies, where the privacy of the participants must be maintained. Most state-of-the-art works using these two modalities perform their association manually, which becomes practically unfeasible as the number of people in the scene increases. We proposed an automatic association method based on a hierarchical linear assignment optimization, which exploits the spatial context of the scene. Moreover, we present extensive experiments on matching from 2 to more than 69 acceleration and video streams, showing significant improvements over a random baseline in a real-world crowded mingling scenario. We also show the effectiveness of our method for incomplete or missing streams (up to a certain limit) and analyze the tradeoff between length of the streams and number of participants. Finally, we provide an analysis of failure cases, showing that deep understanding of the social actions within the context of the event is necessary to further improve performance on this intriguing task.\u3c/p\u3

Sustainability in design: now! Challenges and opportunities for design research, education and practice in the XXI century

Copyright @ 2010 Greenleaf PublicationsLeNS project funded by the Asia Link Programme, EuropeAid, European Commission

Sensory Urbanism Proceedings 2008

This book contains papers from the January 2008 conference, Sensory Urbanism, held by the University of Strathclyde, Glasgow, UK. Papers deal with issues surrounding the sensory perception of urban design and how to design better for all the senses. The book is illustrated throughout, and contains 26 papers from fields including architecture, urban design, environmental psychology, urban design, planning, sound design and more

Cities' Identity Through Architecture and Art

Intended to be a guide for academics, scholars, and interested leaders, this book was designed to critically assess issues related to architectural identity, the city as a scene, the city as an organism, the city as a subject, and the planning or rather approaching of one. A pressing issue for many researchers in the field, the book discusses the negative repercussions resulting from globalization. Studies have indicated that globalization, despite all the positive effects, has resulted in a loss of identity within a city. As a city develops over time, its identity is evolving as well and may even be lost due to rapid and constant changes it is subjected to. Discussed as well are examples and tendencies in dealing with urban identities as well as the transformation of cities and urban cultures mentioned in terms of form, identity, and art. This book is a combination of innovative research submitted to a conference on Cities’ Identity Through Architecture and Arts (CITAA) whereas scholars from all over the world gather in one venue to discuss cultural, historical, and economic issues of the city. Thus, the book offers a collective and global solution that is applicable on a universal level. The research presented in this book was conducted by authors, or rather participants of the conference from, three different continents of the world and organized by IEREK. It was a distinct opportunity for them to share their thoughts with leading scholars and professionals in the field of Architecture, Arts, and Planning. The research and materials in this book are directed at those who are actively engaged in the decision-making processes and to a heterogeneous audience who has an interest to critically examine all the new literature available in the field. A special word of thanks should be made to the editors of this book and to all the authors and co-authors of the chapters who collectively provided the academic community with unique and increasingly valuable literature