1,744 research outputs found
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
Realtime Multilevel Crowd Tracking using Reciprocal Velocity Obstacles
We present a novel, realtime algorithm to compute the trajectory of each
pedestrian in moderately dense crowd scenes. Our formulation is based on an
adaptive particle filtering scheme that uses a multi-agent motion model based
on velocity-obstacles, and takes into account local interactions as well as
physical and personal constraints of each pedestrian. Our method dynamically
changes the number of particles allocated to each pedestrian based on different
confidence metrics. Additionally, we use a new high-definition crowd video
dataset, which is used to evaluate the performance of different pedestrian
tracking algorithms. This dataset consists of videos of indoor and outdoor
scenes, recorded at different locations with 30-80 pedestrians. We highlight
the performance benefits of our algorithm over prior techniques using this
dataset. In practice, our algorithm can compute trajectories of tens of
pedestrians on a multi-core desktop CPU at interactive rates (27-30 frames per
second). To the best of our knowledge, our approach is 4-5 times faster than
prior methods, which provide similar accuracy
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
Adaptive cell-based evacuation systems for leader-follower crowd evacuation
The challenge of controlling crowd movement at large events expands not only to the realm
of emergency evacuations but also to improving non-critical conditions related to operational
efficiency and comfort. In both cases, it becomes necessary to develop adaptive crowd motion
control systems. In particular, adaptive cell-based crowd evacuation systems dynamically
generate exit-choice recommendations favoring a coordinated group dynamic that improves
safety and evacuation time. We investigate the viability of using this mechanism to develop
a ââleader-followerââ evacuation system in which a trained evacuation staff guides evacuees
safely to the exit gates. To validate the proposal, we use a simulationâoptimization framework
integrating microscopic simulation. Evacueesâ behavior has been modeled using a three-layered
architecture that includes eligibility, exit-choice changing, and exit-choice models, calibrated
with hypothetical-choice experiments. As a significant contribution of this work, the proposed
behavior models capture the influence of leaders on evacuees, which is translated into exitchoice
decisions and the adaptation of speed. This influence can be easily modulated to evaluate
the evacuation efficiency under different evacuation scenarios and evacueesâ behavior profiles.
When measuring the efficiency of the evacuation processes, particular attention has been paid
to safety by using pedestrian Macroscopic Fundamental Diagrams (p-MFD), which model the
crowd movement dynamics from a macroscopic perspective. The spatiotemporal view of the
evacuation performance in the form of crowd-pressure vs. density values allowed us to evaluate
and compare safety in different evacuation scenarios reasonably and consistently. Experimental
results confirm the viability of using adaptive cell-based crowd evacuation systems as a guidance
tool to be used by evacuation staff to guide evacuees. Interestingly, we found that evacuation
staff motion speed plays a crucial role in balancing egress time and safety. Thus, it is expected
that by instructing evacuation staff to move at a predefined speed, we can reach the desired
balance between evacuation time, accident probability, and comfort
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