8,741 research outputs found
Serious Games Application for Memory Training Using Egocentric Images
Mild cognitive impairment is the early stage of several neurodegenerative
diseases, such as Alzheimer's. In this work, we address the use of lifelogging
as a tool to obtain pictures from a patient's daily life from an egocentric
point of view. We propose to use them in combination with serious games as a
way to provide a non-pharmacological treatment to improve their quality of
life. To do so, we introduce a novel computer vision technique that classifies
rich and non rich egocentric images and uses them in serious games. We present
results over a dataset composed by 10,997 images, recorded by 7 different
users, achieving 79% of F1-score. Our model presents the first method used for
automatic egocentric images selection applicable to serious games.Comment: 11 page
Egocentric Hand Detection Via Dynamic Region Growing
Egocentric videos, which mainly record the activities carried out by the
users of the wearable cameras, have drawn much research attentions in recent
years. Due to its lengthy content, a large number of ego-related applications
have been developed to abstract the captured videos. As the users are
accustomed to interacting with the target objects using their own hands while
their hands usually appear within their visual fields during the interaction,
an egocentric hand detection step is involved in tasks like gesture
recognition, action recognition and social interaction understanding. In this
work, we propose a dynamic region growing approach for hand region detection in
egocentric videos, by jointly considering hand-related motion and egocentric
cues. We first determine seed regions that most likely belong to the hand, by
analyzing the motion patterns across successive frames. The hand regions can
then be located by extending from the seed regions, according to the scores
computed for the adjacent superpixels. These scores are derived from four
egocentric cues: contrast, location, position consistency and appearance
continuity. We discuss how to apply the proposed method in real-life scenarios,
where multiple hands irregularly appear and disappear from the videos.
Experimental results on public datasets show that the proposed method achieves
superior performance compared with the state-of-the-art methods, especially in
complicated scenarios
The Evolution of First Person Vision Methods: A Survey
The emergence of new wearable technologies such as action cameras and
smart-glasses has increased the interest of computer vision scientists in the
First Person perspective. Nowadays, this field is attracting attention and
investments of companies aiming to develop commercial devices with First Person
Vision recording capabilities. Due to this interest, an increasing demand of
methods to process these videos, possibly in real-time, is expected. Current
approaches present a particular combinations of different image features and
quantitative methods to accomplish specific objectives like object detection,
activity recognition, user machine interaction and so on. This paper summarizes
the evolution of the state of the art in First Person Vision video analysis
between 1997 and 2014, highlighting, among others, most commonly used features,
methods, challenges and opportunities within the field.Comment: First Person Vision, Egocentric Vision, Wearable Devices, Smart
Glasses, Computer Vision, Video Analytics, Human-machine Interactio
Going Deeper into First-Person Activity Recognition
We bring together ideas from recent work on feature design for egocentric
action recognition under one framework by exploring the use of deep
convolutional neural networks (CNN). Recent work has shown that features such
as hand appearance, object attributes, local hand motion and camera ego-motion
are important for characterizing first-person actions. To integrate these ideas
under one framework, we propose a twin stream network architecture, where one
stream analyzes appearance information and the other stream analyzes motion
information. Our appearance stream encodes prior knowledge of the egocentric
paradigm by explicitly training the network to segment hands and localize
objects. By visualizing certain neuron activation of our network, we show that
our proposed architecture naturally learns features that capture object
attributes and hand-object configurations. Our extensive experiments on
benchmark egocentric action datasets show that our deep architecture enables
recognition rates that significantly outperform state-of-the-art techniques --
an average increase in accuracy over all datasets. Furthermore, by
learning to recognize objects, actions and activities jointly, the performance
of individual recognition tasks also increase by (actions) and
(objects). We also include the results of extensive ablative analysis to
highlight the importance of network design decisions.
Semi-Supervised First-Person Activity Recognition in Body-Worn Video
Body-worn cameras are now commonly used for logging daily life, sports, and
law enforcement activities, creating a large volume of archived footage. This
paper studies the problem of classifying frames of footage according to the
activity of the camera-wearer with an emphasis on application to real-world
police body-worn video. Real-world datasets pose a different set of challenges
from existing egocentric vision datasets: the amount of footage of different
activities is unbalanced, the data contains personally identifiable
information, and in practice it is difficult to provide substantial training
footage for a supervised approach. We address these challenges by extracting
features based exclusively on motion information then segmenting the video
footage using a semi-supervised classification algorithm. On publicly available
datasets, our method achieves results comparable to, if not better than,
supervised and/or deep learning methods using a fraction of the training data.
It also shows promising results on real-world police body-worn video
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