3,759 research outputs found
Pedestrian, Crowd, and Evacuation Dynamics
This contribution describes efforts to model the behavior of individual
pedestrians and their interactions in crowds, which generate certain kinds of
self-organized patterns of motion. Moreover, this article focusses on the
dynamics of crowds in panic or evacuation situations, methods to optimize
building designs for egress, and factors potentially causing the breakdown of
orderly motion.Comment: This is a review paper. For related work see http://www.soms.ethz.c
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
Measuring Membership Privacy on Aggregate Location Time-Series
While location data is extremely valuable for various applications,
disclosing it prompts serious threats to individuals' privacy. To limit such
concerns, organizations often provide analysts with aggregate time-series that
indicate, e.g., how many people are in a location at a time interval, rather
than raw individual traces. In this paper, we perform a measurement study to
understand Membership Inference Attacks (MIAs) on aggregate location
time-series, where an adversary tries to infer whether a specific user
contributed to the aggregates.
We find that the volume of contributed data, as well as the regularity and
particularity of users' mobility patterns, play a crucial role in the attack's
success. We experiment with a wide range of defenses based on generalization,
hiding, and perturbation, and evaluate their ability to thwart the attack
vis-a-vis the utility loss they introduce for various mobility analytics tasks.
Our results show that some defenses fail across the board, while others work
for specific tasks on aggregate location time-series. For instance, suppressing
small counts can be used for ranking hotspots, data generalization for
forecasting traffic, hotspot discovery, and map inference, while sampling is
effective for location labeling and anomaly detection when the dataset is
sparse. Differentially private techniques provide reasonable accuracy only in
very specific settings, e.g., discovering hotspots and forecasting their
traffic, and more so when using weaker privacy notions like crowd-blending
privacy. Overall, our measurements show that there does not exist a unique
generic defense that can preserve the utility of the analytics for arbitrary
applications, and provide useful insights regarding the disclosure of sanitized
aggregate location time-series
Human Motion Trajectory Prediction: A Survey
With growing numbers of intelligent autonomous systems in human environments,
the ability of such systems to perceive, understand and anticipate human
behavior becomes increasingly important. Specifically, predicting future
positions of dynamic agents and planning considering such predictions are key
tasks for self-driving vehicles, service robots and advanced surveillance
systems. This paper provides a survey of human motion trajectory prediction. We
review, analyze and structure a large selection of work from different
communities and propose a taxonomy that categorizes existing methods based on
the motion modeling approach and level of contextual information used. We
provide an overview of the existing datasets and performance metrics. We
discuss limitations of the state of the art and outline directions for further
research.Comment: Submitted to the International Journal of Robotics Research (IJRR),
37 page
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