10,758 research outputs found
Anticipatory Mobile Computing: A Survey of the State of the Art and Research Challenges
Today's mobile phones are far from mere communication devices they were ten
years ago. Equipped with sophisticated sensors and advanced computing hardware,
phones can be used to infer users' location, activity, social setting and more.
As devices become increasingly intelligent, their capabilities evolve beyond
inferring context to predicting it, and then reasoning and acting upon the
predicted context. This article provides an overview of the current state of
the art in mobile sensing and context prediction paving the way for
full-fledged anticipatory mobile computing. We present a survey of phenomena
that mobile phones can infer and predict, and offer a description of machine
learning techniques used for such predictions. We then discuss proactive
decision making and decision delivery via the user-device feedback loop.
Finally, we discuss the challenges and opportunities of anticipatory mobile
computing.Comment: 29 pages, 5 figure
Robust Temporally Coherent Laplacian Protrusion Segmentation of 3D Articulated Bodies
In motion analysis and understanding it is important to be able to fit a
suitable model or structure to the temporal series of observed data, in order
to describe motion patterns in a compact way, and to discriminate between them.
In an unsupervised context, i.e., no prior model of the moving object(s) is
available, such a structure has to be learned from the data in a bottom-up
fashion. In recent times, volumetric approaches in which the motion is captured
from a number of cameras and a voxel-set representation of the body is built
from the camera views, have gained ground due to attractive features such as
inherent view-invariance and robustness to occlusions. Automatic, unsupervised
segmentation of moving bodies along entire sequences, in a temporally-coherent
and robust way, has the potential to provide a means of constructing a
bottom-up model of the moving body, and track motion cues that may be later
exploited for motion classification. Spectral methods such as locally linear
embedding (LLE) can be useful in this context, as they preserve "protrusions",
i.e., high-curvature regions of the 3D volume, of articulated shapes, while
improving their separation in a lower dimensional space, making them in this
way easier to cluster. In this paper we therefore propose a spectral approach
to unsupervised and temporally-coherent body-protrusion segmentation along time
sequences. Volumetric shapes are clustered in an embedding space, clusters are
propagated in time to ensure coherence, and merged or split to accommodate
changes in the body's topology. Experiments on both synthetic and real
sequences of dense voxel-set data are shown. This supports the ability of the
proposed method to cluster body-parts consistently over time in a totally
unsupervised fashion, its robustness to sampling density and shape quality, and
its potential for bottom-up model constructionComment: 31 pages, 26 figure
Mobile graphics: SIGGRAPH Asia 2017 course
Peer ReviewedPostprint (published version
Immersive interconnected virtual and augmented reality : a 5G and IoT perspective
Despite remarkable advances, current augmented and virtual reality (AR/VR) applications are a largely individual and local experience. Interconnected AR/VR, where participants can virtually interact across vast distances, remains a distant dream. The great barrier that stands between current technology and such applications is the stringent end-to-end latency requirement, which should not exceed 20 ms in order to avoid motion sickness and other discomforts. Bringing AR/VR to the next level to enable immersive interconnected AR/VR will require significant advances towards 5G ultra-reliable low-latency communication (URLLC) and a Tactile Internet of Things (IoT). In this article, we articulate the technical challenges to enable a future AR/VR end-to-end architecture, that combines 5G URLLC and Tactile IoT technology to support this next generation of interconnected AR/VR applications. Through the use of IoT sensors and actuators, AR/VR applications will be aware of the environmental and user context, supporting human-centric adaptations of the application logic, and lifelike interactions with the virtual environment. We present potential use cases and the required technological building blocks. For each of them, we delve into the current state of the art and challenges that need to be addressed before the dream of remote AR/VR interaction can become reality
Real-Time RGB-D Camera Pose Estimation in Novel Scenes using a Relocalisation Cascade
Camera pose estimation is an important problem in computer vision. Common
techniques either match the current image against keyframes with known poses,
directly regress the pose, or establish correspondences between keypoints in
the image and points in the scene to estimate the pose. In recent years,
regression forests have become a popular alternative to establish such
correspondences. They achieve accurate results, but have traditionally needed
to be trained offline on the target scene, preventing relocalisation in new
environments. Recently, we showed how to circumvent this limitation by adapting
a pre-trained forest to a new scene on the fly. The adapted forests achieved
relocalisation performance that was on par with that of offline forests, and
our approach was able to estimate the camera pose in close to real time. In
this paper, we present an extension of this work that achieves significantly
better relocalisation performance whilst running fully in real time. To achieve
this, we make several changes to the original approach: (i) instead of
accepting the camera pose hypothesis without question, we make it possible to
score the final few hypotheses using a geometric approach and select the most
promising; (ii) we chain several instantiations of our relocaliser together in
a cascade, allowing us to try faster but less accurate relocalisation first,
only falling back to slower, more accurate relocalisation as necessary; and
(iii) we tune the parameters of our cascade to achieve effective overall
performance. These changes allow us to significantly improve upon the
performance our original state-of-the-art method was able to achieve on the
well-known 7-Scenes and Stanford 4 Scenes benchmarks. As additional
contributions, we present a way of visualising the internal behaviour of our
forests and show how to entirely circumvent the need to pre-train a forest on a
generic scene.Comment: Tommaso Cavallari, Stuart Golodetz, Nicholas Lord and Julien Valentin
assert joint first authorshi
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