34,793 research outputs found
A Survey on Content-Aware Video Analysis for Sports
Sports data analysis is becoming increasingly large-scale, diversified, and
shared, but difficulty persists in rapidly accessing the most crucial
information. Previous surveys have focused on the methodologies of sports video
analysis from the spatiotemporal viewpoint instead of a content-based
viewpoint, and few of these studies have considered semantics. This study
develops a deeper interpretation of content-aware sports video analysis by
examining the insight offered by research into the structure of content under
different scenarios. On the basis of this insight, we provide an overview of
the themes particularly relevant to the research on content-aware systems for
broadcast sports. Specifically, we focus on the video content analysis
techniques applied in sportscasts over the past decade from the perspectives of
fundamentals and general review, a content hierarchical model, and trends and
challenges. Content-aware analysis methods are discussed with respect to
object-, event-, and context-oriented groups. In each group, the gap between
sensation and content excitement must be bridged using proper strategies. In
this regard, a content-aware approach is required to determine user demands.
Finally, the paper summarizes the future trends and challenges for sports video
analysis. We believe that our findings can advance the field of research on
content-aware video analysis for broadcast sports.Comment: Accepted for publication in IEEE Transactions on Circuits and Systems
for Video Technology (TCSVT
CHORUS Deliverable 2.1: State of the Art on Multimedia Search Engines
Based on the information provided by European projects and national initiatives related to multimedia search as well as domains experts that participated in the CHORUS Think-thanks and workshops, this document reports on the state of the art related to multimedia content search from, a technical, and socio-economic perspective.
The technical perspective includes an up to date view on content based indexing and retrieval technologies, multimedia search in the context of mobile devices and peer-to-peer networks, and an overview of current evaluation and benchmark inititiatives to measure the performance of multimedia search engines.
From a socio-economic perspective we inventorize the impact and legal consequences of these technical advances and point out future directions of research
Holistic Parameteric Reconstruction of Building Models from Point Clouds
Building models are conventionally reconstructed by building roof points
planar segmentation and then using a topology graph to group the planes
together. Roof edges and vertices are then mathematically represented by
intersecting segmented planes. Technically, such solution is based on
sequential local fitting, i.e., the entire data of one building are not
simultaneously participating in determining the building model. As a
consequence, the solution is lack of topological integrity and geometric rigor.
Fundamentally different from this traditional approach, we propose a holistic
parametric reconstruction method which means taking into consideration the
entire point clouds of one building simultaneously. In our work, building
models are reconstructed from predefined parametric (roof) primitives. We first
use a well-designed deep neural network to segment and identify primitives in
the given building point clouds. A holistic optimization strategy is then
introduced to simultaneously determine the parameters of a segmented primitive.
In the last step, the optimal parameters are used to generate a watertight
building model in CityGML format. The airborne LiDAR dataset RoofN3D with
predefined roof types is used for our test. It is shown that PointNet++ applied
to the entire dataset can achieve an accuracy of 83% for primitive
classification. For a subset of 910 buildings in RoofN3D, the holistic approach
is then used to determine the parameters of primitives and reconstruct the
buildings. The achieved overall quality of reconstruction is 0.08 meters for
point-surface-distance or 0.7 times RMSE of the input LiDAR points. The study
demonstrates the efficiency and capability of the proposed approach and its
potential to handle large scale urban point clouds
Discovery of Latent 3D Keypoints via End-to-end Geometric Reasoning
This paper presents KeypointNet, an end-to-end geometric reasoning framework
to learn an optimal set of category-specific 3D keypoints, along with their
detectors. Given a single image, KeypointNet extracts 3D keypoints that are
optimized for a downstream task. We demonstrate this framework on 3D pose
estimation by proposing a differentiable objective that seeks the optimal set
of keypoints for recovering the relative pose between two views of an object.
Our model discovers geometrically and semantically consistent keypoints across
viewing angles and instances of an object category. Importantly, we find that
our end-to-end framework using no ground-truth keypoint annotations outperforms
a fully supervised baseline using the same neural network architecture on the
task of pose estimation. The discovered 3D keypoints on the car, chair, and
plane categories of ShapeNet are visualized at http://keypointnet.github.io/
Multi-agents Architecture for Semantic Retrieving Video in Distributed Environment
This paper presents an integrated multi-agents architecture for indexing and
retrieving video information.The focus of our work is to elaborate an
extensible approach that gathers a priori almost of the mandatory tools which
palliate to the major intertwining problems raised in the whole process of the
video lifecycle (classification, indexing and retrieval). In fact, effective
and optimal retrieval video information needs a collaborative approach based on
multimodal aspects. Clearly, it must to take into account the distributed
aspect of the data sources, the adaptation of the contents, semantic
annotation, personalized request and active feedback which constitute the
backbone of a vigorous system which improve its performances in a smart wayComment: 11 pages, 11 figures, The Proceeding of International Conference on
Soft Computing and Software Engineering 201
Machine Vision in the Context of Robotics: A Systematic Literature Review
Machine vision is critical to robotics due to a wide range of applications
which rely on input from visual sensors such as autonomous mobile robots and
smart production systems. To create the smart homes and systems of tomorrow, an
overview about current challenges in the research field would be of use to
identify further possible directions, created in a systematic and reproducible
manner. In this work a systematic literature review was conducted covering
research from the last 10 years. We screened 172 papers from four databases and
selected 52 relevant papers. While robustness and computation time were
improved greatly, occlusion and lighting variance are still the biggest
problems faced. From the number of recent publications, we conclude that the
observed field is of relevance and interest to the research community. Further
challenges arise in many areas of the field.Comment: 10 pages 5 figures, systematic literature stud
Ground-based Observations of the Solar Sources of Space Weather (Invited Review)
Monitoring of the Sun and its activity is a task of growing importance in the
frame of space weather research and awareness. Major space weather disturbances
at Earth have their origin in energetic outbursts from the Sun: solar flares,
coronal mass ejections and associated solar energetic particles. In this review
we discuss the importance and complementarity of ground-based and space-based
observations for space weather studies. The main focus is drawn on ground-based
observations in the visible range of the spectrum, in particular in the
diagnostically manifold H spectral line, which enables us to detect and
study solar flares, filaments, filament eruptions, and Moreton waves. Existing
H networks such as the GONG and the Global High-Resolution H
Network are discussed. As an example of solar observations from space weather
research to operations, we present the system of real-time detection of
H flares and filaments established at Kanzelh\"ohe Observatory (KSO;
Austria) in the frame of the ESA Space Situational Awareness programme. During
the evaluation period 7/2013 - 11/2015, KSO provided 3020 hours of real-time
H observations at the SWE portal. In total, 824 H flares were
detected and classified by the real-time detection system, including 174 events
of H importance class 1 and larger. For the total sample of events,
95\% of the automatically determined flare peak times lie within 5 min of
the values given in the official optical flares reports (by NOAA and KSO), and
76\% of the start times. The heliographic positions determined are better than
5. The probability of detection of flares of importance 1 or
larger is 95\%, with a false alarm rate of 16\%. These numbers confirm the high
potential of automatic flare detection and alerting from ground-based
observatories.Comment: Accepted for "Ground-based Solar Observations in the Space
Instrumentation Era", Proceedings of the Coimbra Solar Physics Meeting 2015,
ASP Conference Series, Eds. I. Dorotovic, C. Fischer, and M. Temmer; 16p
Automatic Image Filtering on Social Networks Using Deep Learning and Perceptual Hashing During Crises
The extensive use of social media platforms, especially during disasters,
creates unique opportunities for humanitarian organizations to gain situational
awareness and launch relief operations accordingly. In addition to the textual
content, people post overwhelming amounts of imagery data on social networks
within minutes of a disaster hit. Studies point to the importance of this
online imagery content for emergency response. Despite recent advances in the
computer vision field, automatic processing of the crisis-related social media
imagery data remains a challenging task. It is because a majority of which
consists of redundant and irrelevant content. In this paper, we present an
image processing pipeline that comprises de-duplication and relevancy filtering
mechanisms to collect and filter social media image content in real-time during
a crisis event. Results obtained from extensive experiments on real-world
crisis datasets demonstrate the significance of the proposed pipeline for
optimal utilization of both human and machine computing resources.Comment: Accepted for publication in the 14th International Conference on
Information Systems For Crisis Response and Management (ISCRAM), 201
A state of the art of urban reconstruction: street, street network, vegetation, urban feature
World population is raising, especially the part of people living in cities.
With increased population and complex roles regarding their inhabitants and
their surroundings, cities concentrate difficulties for design, planning and
analysis. These tasks require a way to reconstruct/model a city. Traditionally,
much attention has been given to buildings reconstruction, yet an essential
part of city were neglected: streets. Streets reconstruction has been seldom
researched. Streets are also complex compositions of urban features, and have a
unique role for transportation (as they comprise roads). We aim at completing
the recent state of the art for building reconstruction (Musialski2012) by
considering all other aspect of urban reconstruction. We introduce the need for
city models. Because reconstruction always necessitates data, we first analyse
which data are available. We then expose a state of the art of street
reconstruction, street network reconstruction, urban features
reconstruction/modelling, vegetation , and urban objects
reconstruction/modelling.
Although reconstruction strategies vary widely, we can order them by the role
the model plays, from data driven approach, to model-based approach, to inverse
procedural modelling and model catalogue matching. The main challenges seems to
come from the complex nature of urban environment and from the limitations of
the available data. Urban features have strong relationships, between them, and
to their surrounding, as well as in hierarchical relations. Procedural
modelling has the power to express these relations, and could be applied to the
reconstruction of urban features via the Inverse Procedural Modelling paradigm.Comment: Extracted from PhD (chap1
Image Provenance Analysis at Scale
Prior art has shown it is possible to estimate, through image processing and
computer vision techniques, the types and parameters of transformations that
have been applied to the content of individual images to obtain new images.
Given a large corpus of images and a query image, an interesting further step
is to retrieve the set of original images whose content is present in the query
image, as well as the detailed sequences of transformations that yield the
query image given the original images. This is a problem that recently has
received the name of image provenance analysis. In these times of public media
manipulation ( e.g., fake news and meme sharing), obtaining the history of
image transformations is relevant for fact checking and authorship
verification, among many other applications. This article presents an
end-to-end processing pipeline for image provenance analysis, which works at
real-world scale. It employs a cutting-edge image filtering solution that is
custom-tailored for the problem at hand, as well as novel techniques for
obtaining the provenance graph that expresses how the images, as nodes, are
ancestrally connected. A comprehensive set of experiments for each stage of the
pipeline is provided, comparing the proposed solution with state-of-the-art
results, employing previously published datasets. In addition, this work
introduces a new dataset of real-world provenance cases from the social media
site Reddit, along with baseline results.Comment: 13 pages, 6 figure
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