891 research outputs found
Ambient Intelligence for Next-Generation AR
Next-generation augmented reality (AR) promises a high degree of
context-awareness - a detailed knowledge of the environmental, user, social and
system conditions in which an AR experience takes place. This will facilitate
both the closer integration of the real and virtual worlds, and the provision
of context-specific content or adaptations. However, environmental awareness in
particular is challenging to achieve using AR devices alone; not only are these
mobile devices' view of an environment spatially and temporally limited, but
the data obtained by onboard sensors is frequently inaccurate and incomplete.
This, combined with the fact that many aspects of core AR functionality and
user experiences are impacted by properties of the real environment, motivates
the use of ambient IoT devices, wireless sensors and actuators placed in the
surrounding environment, for the measurement and optimization of environment
properties. In this book chapter we categorize and examine the wide variety of
ways in which these IoT sensors and actuators can support or enhance AR
experiences, including quantitative insights and proof-of-concept systems that
will inform the development of future solutions. We outline the challenges and
opportunities associated with several important research directions which must
be addressed to realize the full potential of next-generation AR.Comment: This is a preprint of a book chapter which will appear in the
Springer Handbook of the Metavers
A Survey of Deep Learning in Sports Applications: Perception, Comprehension, and Decision
Deep learning has the potential to revolutionize sports performance, with
applications ranging from perception and comprehension to decision. This paper
presents a comprehensive survey of deep learning in sports performance,
focusing on three main aspects: algorithms, datasets and virtual environments,
and challenges. Firstly, we discuss the hierarchical structure of deep learning
algorithms in sports performance which includes perception, comprehension and
decision while comparing their strengths and weaknesses. Secondly, we list
widely used existing datasets in sports and highlight their characteristics and
limitations. Finally, we summarize current challenges and point out future
trends of deep learning in sports. Our survey provides valuable reference
material for researchers interested in deep learning in sports applications
Indoor navigation systems based on data mining techniques in internet of things: a survey
© 2018, Springer Science+Business Media, LLC, part of Springer Nature. Internet of Things (IoT) is turning into an essential part of daily life, and numerous IoT-based scenarios will be seen in future of modern cities ranging from small indoor situations to huge outdoor environments. In this era, navigation continues to be a crucial element in both outdoor and indoor environments, and many solutions have been provided in both cases. On the other side, recent smart objects have produced a substantial amount of various data which demands sophisticated data mining solutions to cope with them. This paper presents a detailed review of previous studies on using data mining techniques in indoor navigation systems for the loT scenarios. We aim to understand what type of navigation problems exist in different IoT scenarios with a focus on indoor environments and later on we investigate how data mining solutions can provide solutions on those challenges
An Outlook into the Future of Egocentric Vision
What will the future be? We wonder! In this survey, we explore the gap
between current research in egocentric vision and the ever-anticipated future,
where wearable computing, with outward facing cameras and digital overlays, is
expected to be integrated in our every day lives. To understand this gap, the
article starts by envisaging the future through character-based stories,
showcasing through examples the limitations of current technology. We then
provide a mapping between this future and previously defined research tasks.
For each task, we survey its seminal works, current state-of-the-art
methodologies and available datasets, then reflect on shortcomings that limit
its applicability to future research. Note that this survey focuses on software
models for egocentric vision, independent of any specific hardware. The paper
concludes with recommendations for areas of immediate explorations so as to
unlock our path to the future always-on, personalised and life-enhancing
egocentric vision.Comment: We invite comments, suggestions and corrections here:
https://openreview.net/forum?id=V3974SUk1
Edge Video Analytics: A Survey on Applications, Systems and Enabling Techniques
Video, as a key driver in the global explosion of digital information, can
create tremendous benefits for human society. Governments and enterprises are
deploying innumerable cameras for a variety of applications, e.g., law
enforcement, emergency management, traffic control, and security surveillance,
all facilitated by video analytics (VA). This trend is spurred by the rapid
advancement of deep learning (DL), which enables more precise models for object
classification, detection, and tracking. Meanwhile, with the proliferation of
Internet-connected devices, massive amounts of data are generated daily,
overwhelming the cloud. Edge computing, an emerging paradigm that moves
workloads and services from the network core to the network edge, has been
widely recognized as a promising solution. The resulting new intersection, edge
video analytics (EVA), begins to attract widespread attention. Nevertheless,
only a few loosely-related surveys exist on this topic. The basic concepts of
EVA (e.g., definition, architectures) were not fully elucidated due to the
rapid development of this domain. To fill these gaps, we provide a
comprehensive survey of the recent efforts on EVA. In this paper, we first
review the fundamentals of edge computing, followed by an overview of VA. The
EVA system and its enabling techniques are discussed next. In addition, we
introduce prevalent frameworks and datasets to aid future researchers in the
development of EVA systems. Finally, we discuss existing challenges and foresee
future research directions. We believe this survey will help readers comprehend
the relationship between VA and edge computing, and spark new ideas on EVA.Comment: 31 pages, 13 figure
Human Action Recognition and Monitoring in Ambient Assisted Living Environments
Population ageing is set to become one of the most significant challenges of the 21st century, with implications for almost all sectors of society. Especially in developed countries, governments should immediately implement policies and solutions to facilitate the needs of an increasingly older population. Ambient Intelligence (AmI) and in particular the area of Ambient Assisted Living (AAL) offer a feasible response, allowing the creation of human-centric smart environments that are sensitive and responsive to the needs and behaviours of the user.
In such a scenario, understand what a human being is doing, if and how he/she is interacting with specific objects, or whether abnormal situations are occurring is critical.
This thesis is focused on two related research areas of AAL: the development of innovative vision-based techniques for human action recognition and the remote monitoring of users behaviour in smart environments.
The former topic is addressed through different approaches based on data extracted from RGB-D sensors.
A first algorithm exploiting skeleton joints orientations is proposed. This approach is extended through a multi-modal strategy that includes the RGB channel to define a number of temporal images, capable of describing the time evolution of actions.
Finally, the concept of template co-updating concerning action recognition is introduced. Indeed, exploiting different data categories (e.g., skeleton and RGB information) improve the effectiveness of template updating through co-updating techniques.
The action recognition algorithms have been evaluated on CAD-60 and CAD-120, achieving results comparable with the state-of-the-art. Moreover, due to the lack of datasets including skeleton joints orientations, a new benchmark named Office Activity Dataset has been internally acquired and released.
Regarding the second topic addressed, the goal is to provide a detailed implementation strategy concerning a generic Internet of Things monitoring platform that could be used for checking users' behaviour in AmI/AAL contexts
Locomotion Traces Data Mining for Supporting Frail People with Cognitive Impairment
The rapid increase in the senior population is posing serious challenges to national healthcare systems. Hence, innovative tools are needed to early detect health issues, including cognitive decline. Several clinical studies show that it is possible to identify cognitive impairment based on the locomotion patterns of older people. Thus, this thesis at first focused on providing a systematic literature review of locomotion data mining systems for supporting Neuro-Degenerative Diseases (NDD) diagnosis, identifying locomotion anomaly indicators and movement patterns for discovering low-level locomotion indicators, sensor data acquisition, and processing methods, as well as NDD detection algorithms considering their pros and cons. Then, we investigated the use of sensor data and Deep Learning (DL) to recognize abnormal movement patterns in instrumented smart-homes. In order to get rid of the noise introduced by indoor constraints and activity execution, we introduced novel visual feature extraction methods for locomotion data.
Our solutions rely on locomotion traces segmentation, image-based extraction of salient features from locomotion segments, and vision-based DL. Furthermore, we proposed a data augmentation strategy to increase the volume of collected data and generalize the solution to different smart-homes with different layouts. We carried out extensive experiments with a large real-world dataset acquired in a smart-home test-bed from older
people, including people with cognitive diseases. Experimental comparisons show that our system outperforms state-of-the-art methods
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