5,682 research outputs found
VIRD: Immersive Match Video Analysis for High-Performance Badminton Coaching
Badminton is a fast-paced sport that requires a strategic combination of
spatial, temporal, and technical tactics. To gain a competitive edge at
high-level competitions, badminton professionals frequently analyze match
videos to gain insights and develop game strategies. However, the current
process for analyzing matches is time-consuming and relies heavily on manual
note-taking, due to the lack of automatic data collection and appropriate
visualization tools. As a result, there is a gap in effectively analyzing
matches and communicating insights among badminton coaches and players. This
work proposes an end-to-end immersive match analysis pipeline designed in close
collaboration with badminton professionals, including Olympic and national
coaches and players. We present VIRD, a VR Bird (i.e., shuttle) immersive
analysis tool, that supports interactive badminton game analysis in an
immersive environment based on 3D reconstructed game views of the match video.
We propose a top-down analytic workflow that allows users to seamlessly move
from a high-level match overview to a detailed game view of individual rallies
and shots, using situated 3D visualizations and video. We collect 3D spatial
and dynamic shot data and player poses with computer vision models and
visualize them in VR. Through immersive visualizations, coaches can
interactively analyze situated spatial data (player positions, poses, and shot
trajectories) with flexible viewpoints while navigating between shots and
rallies effectively with embodied interaction. We evaluated the usefulness of
VIRD with Olympic and national-level coaches and players in real matches.
Results show that immersive analytics supports effective badminton match
analysis with reduced context-switching costs and enhances spatial
understanding with a high sense of presence.Comment: To Appear in IEEE Transactions on Visualization and Computer Graphics
(IEEE VIS), 202
Introduction to Facial Micro Expressions Analysis Using Color and Depth Images: A Matlab Coding Approach (Second Edition, 2023)
The book attempts to introduce a gentle introduction to the field of Facial
Micro Expressions Recognition (FMER) using Color and Depth images, with the aid
of MATLAB programming environment. FMER is a subset of image processing and it
is a multidisciplinary topic to analysis. So, it requires familiarity with
other topics of Artifactual Intelligence (AI) such as machine learning, digital
image processing, psychology and more. So, it is a great opportunity to write a
book which covers all of these topics for beginner to professional readers in
the field of AI and even without having background of AI. Our goal is to
provide a standalone introduction in the field of MFER analysis in the form of
theorical descriptions for readers with no background in image processing with
reproducible Matlab practical examples. Also, we describe any basic definitions
for FMER analysis and MATLAB library which is used in the text, that helps
final reader to apply the experiments in the real-world applications. We
believe that this book is suitable for students, researchers, and professionals
alike, who need to develop practical skills, along with a basic understanding
of the field. We expect that, after reading this book, the reader feels
comfortable with different key stages such as color and depth image processing,
color and depth image representation, classification, machine learning, facial
micro-expressions recognition, feature extraction and dimensionality reduction.
The book attempts to introduce a gentle introduction to the field of Facial
Micro Expressions Recognition (FMER) using Color and Depth images, with the aid
of MATLAB programming environment.Comment: This is the second edition of the boo
Interactive visualizations of unstructured oceanographic data
The newly founded company Oceanbox is creating a novel oceanographic forecasting system to provide oceanography as a service. These services use mathematical models that generate large hydrodynamic data sets as unstructured triangular grids with high-resolution model areas. Oceanbox makes the model results accessible in a web application. New visualizations are needed to accommodate land-masking and large data volumes.
In this thesis, we propose using a k-d tree to spatially partition unstructured triangular grids to provide the look-up times needed for interactive visualizations. A k-d tree is implemented in F# called FsKDTree. This thesis also describes the implementation of dynamic tiling map layers to visualize current barbs, scalar fields, and particle streams. The current barb layer queries data from the data server with the help of the k-d tree and displays it in the browser. Scalar fields and particle streams are implemented using WebGL, which enables the rendering of triangular grids. Stream particle visualization effects are implemented as velocity advection computed on the GPU with textures.
The new visualizations are used in Oceanbox's production systems, and spatial indexing has been integrated into Oceanbox's archive retrieval system. FsKDTree improves tree creation times by up to 4x over the C# equivalent and improves search times by up to 13x compared to the .NET C# implementation. Finally, the largest model areas can be viewed with current barbs, scalar fields, and particle stream visualizations at 60 FPS, even for the largest model areas provided by the service
Mathematical Problems in Rock Mechanics and Rock Engineering
With increasing requirements for energy, resources and space, rock engineering projects are being constructed more often and are operated in large-scale environments with complex geology. Meanwhile, rock failures and rock instabilities occur more frequently, and severely threaten the safety and stability of rock engineering projects. It is well-recognized that rock has multi-scale structures and involves multi-scale fracture processes. Meanwhile, rocks are commonly subjected simultaneously to complex static stress and strong dynamic disturbance, providing a hotbed for the occurrence of rock failures. In addition, there are many multi-physics coupling processes in a rock mass. It is still difficult to understand these rock mechanics and characterize rock behavior during complex stress conditions, multi-physics processes, and multi-scale changes. Therefore, our understanding of rock mechanics and the prevention and control of failure and instability in rock engineering needs to be furthered. The primary aim of this Special Issue “Mathematical Problems in Rock Mechanics and Rock Engineering” is to bring together original research discussing innovative efforts regarding in situ observations, laboratory experiments and theoretical, numerical, and big-data-based methods to overcome the mathematical problems related to rock mechanics and rock engineering. It includes 12 manuscripts that illustrate the valuable efforts for addressing mathematical problems in rock mechanics and rock engineering
Soundscape in Urban Forests
This Special Issue of Forests explores the role of soundscapes in urban forested areas. It is comprised of 11 papers involving soundscape studies conducted in urban forests from Asia and Africa. This collection contains six research fields: (1) the ecological patterns and processes of forest soundscapes; (2) the boundary effects and perceptual topology; (3) natural soundscapes and human health; (4) the experience of multi-sensory interactions; (5) environmental behavior and cognitive disposition; and (6) soundscape resource management in forests
Building Scalable Video Understanding Benchmarks through Sports
Existing benchmarks for evaluating long video understanding falls short on
two critical aspects, either lacking in scale or quality of annotations. These
limitations arise from the difficulty in collecting dense annotations for long
videos, which often require manually labeling each frame. In this work, we
introduce an automated Annotation and Video Stream Alignment Pipeline
(abbreviated ASAP). We demonstrate the generality of ASAP by aligning unlabeled
videos of four different sports with corresponding freely available dense web
annotations (i.e. commentary). We then leverage ASAP scalability to create
LCric, a large-scale long video understanding benchmark, with over 1000 hours
of densely annotated long Cricket videos (with an average sample length of ~50
mins) collected at virtually zero annotation cost. We benchmark and analyze
state-of-the-art video understanding models on LCric through a large set of
compositional multi-choice and regression queries. We establish a human
baseline that indicates significant room for new research to explore. Our human
studies indicate that ASAP can align videos and annotations with high fidelity,
precision, and speed. The dataset along with the code for ASAP and baselines
can be accessed here: https://asap-benchmark.github.io/
Catherine Colomb’s VISION OF TIME: in Dialogue with Marcel Proust and Virginia Woolf
This monograph is the first substantial contribution to the study of the Swiss novelist Catherine Colomb’s dialogue with Marcel Proust and Virginia Woolf as well as to time and memory studies. The framework and approach devised to examine Colomb’s oeuvre contribute to unravelling some of its complexities, not only in its curving style, ephemeral, and sequence-defying narrative, but also in its literary engagement with the science and philosophy that shaped modernity and proposed new ways of thinking time, knowledge, and the human experience. This thesis ultimately allows us to gain insight into the originality of Colombian time experience, memory, and point-of-view representations, transcending the alleged influence of her iconic predecessors
Geo-Information Harvesting from Social Media Data
As unconventional sources of geo-information, massive imagery and text messages from open platforms and social media form a temporally quasi-seamless, spatially multiperspective stream, but with unknown and diverse quality. Due to its complementarity to remote sensing data, geo-information from these sources offers promising perspectives, but harvesting is not trivial due to its data characteristics. In this article, we address key aspects in the field, including data availability, analysisready data preparation and data management, geo-information extraction from social media text messages and images, and the fusion of social media and remote sensing data. We then showcase some exemplary geographic applications. In addition, we present the first extensive discussion of ethical considerations of social media data in the context of geo-information harvesting and geographic applications. With this effort, we wish to stimulate curiosity and lay the groundwork for researchers who intend to explore social media data for geo-applications. We encourage the community to join forces by sharing their code and data
A review of abnormal behavior detection in activities of daily living
Abnormal behavior detection (ABD) systems are built to automatically identify and recognize abnormal behavior from various input data types, such as sensor-based and vision-based input. As much as the attention received for ABD systems, the number of studies on ABD in activities of daily living (ADL) is limited. Owing to the increasing rate of elderly accidents in the home compound, ABD in ADL research should be given as much attention to preventing accidents by sending out signals when abnormal behavior such as falling is detected. In this study, we compare and contrast the formation of the ABD system in ADL from input data types (sensor-based input and vision-based input) to modeling techniques (conventional and deep learning approaches). We scrutinize the public datasets available and provide solutions for one of the significant issues: the lack of datasets in ABD in ADL. This work aims to guide new research to understand the field of ABD in ADL better and serve as a reference for future study of better Ambient Assisted Living with the growing smart home trend
Knowledge Graph Building Blocks: An easy-to-use Framework for developing FAIREr Knowledge Graphs
Knowledge graphs and ontologies provide promising technical solutions for
implementing the FAIR Principles for Findable, Accessible, Interoperable, and
Reusable data and metadata. However, they also come with their own challenges.
Nine such challenges are discussed and associated with the criterion of
cognitive interoperability and specific FAIREr principles (FAIR + Explorability
raised) that they fail to meet. We introduce an easy-to-use, open source
knowledge graph framework that is based on knowledge graph building blocks
(KGBBs). KGBBs are small information modules for knowledge-processing, each
based on a specific type of semantic unit. By interrelating several KGBBs, one
can specify a KGBB-driven FAIREr knowledge graph. Besides implementing semantic
units, the KGBB Framework clearly distinguishes and decouples an internal
in-memory data model from data storage, data display, and data access/export
models. We argue that this decoupling is essential for solving many problems of
knowledge management systems. We discuss the architecture of the KGBB Framework
as we envision it, comprising (i) an openly accessible KGBB-Repository for
different types of KGBBs, (ii) a KGBB-Engine for managing and operating FAIREr
knowledge graphs (including automatic provenance tracking, editing changelog,
and versioning of semantic units); (iii) a repository for KGBB-Functions; (iv)
a low-code KGBB-Editor with which domain experts can create new KGBBs and
specify their own FAIREr knowledge graph without having to think about semantic
modelling. We conclude with discussing the nine challenges and how the KGBB
Framework provides solutions for the issues they raise. While most of what we
discuss here is entirely conceptual, we can point to two prototypes that
demonstrate the principle feasibility of using semantic units and KGBBs to
manage and structure knowledge graphs
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