992 research outputs found
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
Real-Time Predictive Modeling and Robust Avoidance of Pedestrians with Uncertain, Changing Intentions
To plan safe trajectories in urban environments, autonomous vehicles must be
able to quickly assess the future intentions of dynamic agents. Pedestrians are
particularly challenging to model, as their motion patterns are often uncertain
and/or unknown a priori. This paper presents a novel changepoint detection and
clustering algorithm that, when coupled with offline unsupervised learning of a
Gaussian process mixture model (DPGP), enables quick detection of changes in
intent and online learning of motion patterns not seen in prior training data.
The resulting long-term movement predictions demonstrate improved accuracy
relative to offline learning alone, in terms of both intent and trajectory
prediction. By embedding these predictions within a chance-constrained motion
planner, trajectories which are probabilistically safe to pedestrian motions
can be identified in real-time. Hardware experiments demonstrate that this
approach can accurately predict pedestrian motion patterns from onboard
sensor/perception data and facilitate robust navigation within a dynamic
environment.Comment: Submitted to 2014 International Workshop on the Algorithmic
Foundations of Robotic
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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