1,252 research outputs found
Route Packing: Geospatially-Accurate Visualization of Route Networks
We present route packing}, a novel (geo)visualization technique for displaying several routes simultaneously on a geographic map while preserving the geospatial layout, identity, directionality, and volume of individual routes. The technique collects variable-width route lines side by side while minimizing crossings, encodes them with categorical colors, and decorates them with glyphs to show their directions. Furthermore, nodes representing sources and sinks use glyphs to indicate whether routes stop at the node or merely pass through it. We conducted a crowd-sourced user study investigating route tracing performance with road networks visualized using our route packing technique. Our findings highlight the visual parameters under which the technique yields optimal performance
Metric-Scale Truncation-Robust Heatmaps for 3D Human Pose Estimation
Heatmap representations have formed the basis of 2D human pose estimation
systems for many years, but their generalizations for 3D pose have only
recently been considered. This includes 2.5D volumetric heatmaps, whose X and Y
axes correspond to image space and the Z axis to metric depth around the
subject. To obtain metric-scale predictions, these methods must include a
separate, explicit post-processing step to resolve scale ambiguity. Further,
they cannot encode body joint positions outside of the image boundaries,
leading to incomplete pose estimates in case of image truncation. We address
these limitations by proposing metric-scale truncation-robust (MeTRo)
volumetric heatmaps, whose dimensions are defined in metric 3D space near the
subject, instead of being aligned with image space. We train a
fully-convolutional network to estimate such heatmaps from monocular RGB in an
end-to-end manner. This reinterpretation of the heatmap dimensions allows us to
estimate complete metric-scale poses without test-time knowledge of the focal
length or person distance and without relying on anthropometric heuristics in
post-processing. Furthermore, as the image space is decoupled from the heatmap
space, the network can learn to reason about joints beyond the image boundary.
Using ResNet-50 without any additional learned layers, we obtain
state-of-the-art results on the Human3.6M and MPI-INF-3DHP benchmarks. As our
method is simple and fast, it can become a useful component for real-time
top-down multi-person pose estimation systems. We make our code publicly
available to facilitate further research (see
https://vision.rwth-aachen.de/metro-pose3d).Comment: Accepted for publication at the 2020 IEEE Conference on Automatic
Face and Gesture Recognition (FG
APREGOAR: Development of a geospatial database applied to local news in Lisbon
Project Work presented as the partial requirement for obtaining a Master's degree in Geographic Information Systems and ScienceHá informações valiosas em formato de texto não estruturado sobre a localização, calendarização
e a essências dos eventos disponíveis no conteúdo de notícias digitais. Vários
trabalhos em curso já tentam extrair detalhes de eventos de fontes de notícias digitais,
mas muitas vezes não com a nuance necssária para representar com precisão onde as
coisas realmente acontecem. Alternativamente, os jornalistas poderiam associar manualmente
atributos a eventos descritos nos seus artigos enquanto publicam, melhorando a
exatidão e a confiança nestes atributos espaciais e temporais. Estes atributos poderiam
então estar imediatamente disponíveis para avaliar a cobertura temática, temporal e
espacial do conteúdo de uma agência, bem como melhorar a experiência do utilizador
na exploração do conteúdo, fornecendo dimensões adicionais que podem ser filtradas.
Embora a tecnologia de atribuição de dimensões geoespaciais e temporais para o
emprego de aplicaçãoes voltadas para o consumidor não seja novidade, tem ainda de
ser aplicada à escala das notícias. Além disso, a maioria dos sistemas existentes suporta
apenas uma definição pontual da localização dos artigos, que pode não representar bem
o(s) local(is) real(ais) dos eventos descritos.
Este trabalho define uma aplicação web de código aberto e uma base de dados
espacial subjacente que suporta i) a associação de múltiplos polígonos a representar
o local onde cada evento ocorre, os prazos associados aos eventos, em linha com os
atributos temáticos tradicionais associados aos artigos de notícias; ii) a contextualização
de cada artigo através da adição de mapas de eventos em linha para esclarecer aos
leitores onde os eventos do artigo ocorrem; e iii) a exploração dos corpora adicionados
através de filtros temáticos, espaciais e temporais que exibem os resultados em mapas
de cobertura interactivos e listas de artigos e eventos.
O projeto foi aplicado na área da grande Lisboa de Portugal. Para além da funcionalidade
acima referida, este projeto constroi gazetteers progressivos que podem ser
reutilizados como associações de lugares, ou para uma meta-análise mais aprofundada
do lugar, tal como é percebido coloquialmente. Demonstra a facilidade com que estas
dimensões adicionais podem ser incorporadas com grade confiança na precisão da definição, geridas, e alavancadas para melhorar a gestão de conteúdo das agências noticiosas,
a compreensão dos leitores, a exploração dos investigadores, ou extraídas para
combinação com outros conjuntos dos dados para fornecer conhecimentos adicionais.There is valuable information in unstructured text format about the location, timing,
and nature of events available in digital news content. Several ongoing efforts already
attempt to extract event details from digital news sources, but often not with the
nuance needed to accurately represent the where things actually happen. Alternatively,
journalists could manually associate attributes to events described in their articles while
publishing, improving accuracy and confidence in these spatial and temporal attributes.
These attributes could then be immediately available for evaluating thematic, temporal,
and spatial coverage of an agency’s content, as well as improve the user experience of
content exploration by providing additional dimensions that can be filtered.
Though the technology of assigning geospatial and temporal dimensions for the
employ of consumer-facing applications is not novel, it has yet to be applied at scale to
the news. Additionally, most existing systems only support a single point definition of
article locations, which may not well represent the actual place(s) of events described
within.
This work defines an open source web application and underlying spatial database
that supports i) the association of multiple polygons representing where each event
occurs, time frames associated with the events, inline with the traditional thematic
attributes associated with news articles; ii) the contextualization of each article via the
addition of inline event maps to clarify to readers where the events of the article occur;
and iii) the exploration of the added corpora via thematic, spatial, and temporal filters
that display results in interactive coverage maps and lists of articles and events.
The project was applied to the greater Lisbon area of Portugal. In addition to the
above functionality, this project builds progressive gazetteers that can be reused as place
associations, or for further meta analysis of place as it is colloquially understood. It
demonstrates the ease of which these additional dimensions may be incorporated with a
high confidence in definition accuracy, managed, and leveraged to improve news agency
content management, reader understanding, researcher exploration, or extracted for
combination with other datasets to provide additional insights
MeTRAbs: Metric-Scale Truncation-Robust Heatmaps for Absolute 3D Human Pose Estimation
Heatmap representations have formed the basis of human pose estimation
systems for many years, and their extension to 3D has been a fruitful line of
recent research. This includes 2.5D volumetric heatmaps, whose X and Y axes
correspond to image space and Z to metric depth around the subject. To obtain
metric-scale predictions, 2.5D methods need a separate post-processing step to
resolve scale ambiguity. Further, they cannot localize body joints outside the
image boundaries, leading to incomplete estimates for truncated images. To
address these limitations, we propose metric-scale truncation-robust (MeTRo)
volumetric heatmaps, whose dimensions are all defined in metric 3D space,
instead of being aligned with image space. This reinterpretation of heatmap
dimensions allows us to directly estimate complete, metric-scale poses without
test-time knowledge of distance or relying on anthropometric heuristics, such
as bone lengths. To further demonstrate the utility our representation, we
present a differentiable combination of our 3D metric-scale heatmaps with 2D
image-space ones to estimate absolute 3D pose (our MeTRAbs architecture). We
find that supervision via absolute pose loss is crucial for accurate
non-root-relative localization. Using a ResNet-50 backbone without further
learned layers, we obtain state-of-the-art results on Human3.6M, MPI-INF-3DHP
and MuPoTS-3D. Our code will be made publicly available to facilitate further
research.Comment: See project page at https://vision.rwth-aachen.de/metrabs . Accepted
for publication in the IEEE Transactions on Biometrics, Behavior, and
Identity Science (TBIOM), Special Issue "Selected Best Works From Automated
Face and Gesture Recognition 2020". Extended version of FG paper
arXiv:2003.0295
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