15,603 research outputs found
Guidelines for assessing pedestrian evacuation software applications
This paper serves to clearly identify and explain criteria to consider when evaluating the
suitability of a pedestrian evacuation software application to assess the evacuation
process of a building. Guidelines in the form of nine topic areas identify different
modelling approaches adopted, as well as features / functionality provided by
applications designed specifically for simulating the egress of pedestrians from inside a
building. The paper concludes with a synopsis of these guidelines, identifying key
questions (by topic area) to found an evaluation
LCrowdV: Generating Labeled Videos for Simulation-based Crowd Behavior Learning
We present a novel procedural framework to generate an arbitrary number of
labeled crowd videos (LCrowdV). The resulting crowd video datasets are used to
design accurate algorithms or training models for crowded scene understanding.
Our overall approach is composed of two components: a procedural simulation
framework for generating crowd movements and behaviors, and a procedural
rendering framework to generate different videos or images. Each video or image
is automatically labeled based on the environment, number of pedestrians,
density, behavior, flow, lighting conditions, viewpoint, noise, etc.
Furthermore, we can increase the realism by combining synthetically-generated
behaviors with real-world background videos. We demonstrate the benefits of
LCrowdV over prior lableled crowd datasets by improving the accuracy of
pedestrian detection and crowd behavior classification algorithms. LCrowdV
would be released on the WWW
Accessibility and urban design - Knowledge matters
Copyright @ 2009 Birmingham City University Publicatio
Forecasting People Trajectories and Head Poses by Jointly Reasoning on Tracklets and Vislets
In this work, we explore the correlation between people trajectories and
their head orientations. We argue that people trajectory and head pose
forecasting can be modelled as a joint problem. Recent approaches on trajectory
forecasting leverage short-term trajectories (aka tracklets) of pedestrians to
predict their future paths. In addition, sociological cues, such as expected
destination or pedestrian interaction, are often combined with tracklets. In
this paper, we propose MiXing-LSTM (MX-LSTM) to capture the interplay between
positions and head orientations (vislets) thanks to a joint unconstrained
optimization of full covariance matrices during the LSTM backpropagation. We
additionally exploit the head orientations as a proxy for the visual attention,
when modeling social interactions. MX-LSTM predicts future pedestrians location
and head pose, increasing the standard capabilities of the current approaches
on long-term trajectory forecasting. Compared to the state-of-the-art, our
approach shows better performances on an extensive set of public benchmarks.
MX-LSTM is particularly effective when people move slowly, i.e. the most
challenging scenario for all other models. The proposed approach also allows
for accurate predictions on a longer time horizon.Comment: Accepted at IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE
INTELLIGENCE 2019. arXiv admin note: text overlap with arXiv:1805.0065
Promoting Bicycle Commuter Safety, Research Report 11-08
We present an overview of the risks associated with cycling to emphasize the need for safety. We focus on the application of frameworks from social psychology to education, one of the 5 Es—engineering, education, enforcement, encouragement, and evaluation. We use the structure of the 5 Es to organize information with particular attention to engineering and education in the literature review. Engineering is essential because the infrastructure is vital to protecting cyclists. Education is emphasized since the central focus of the report is safety
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