289 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
A Data-driven Model for Interaction-aware Pedestrian Motion Prediction in Object Cluttered Environments
This paper reports on a data-driven, interaction-aware motion prediction
approach for pedestrians in environments cluttered with static obstacles. When
navigating in such workspaces shared with humans, robots need accurate motion
predictions of the surrounding pedestrians. Human navigation behavior is mostly
influenced by their surrounding pedestrians and by the static obstacles in
their vicinity. In this paper we introduce a new model based on Long-Short Term
Memory (LSTM) neural networks, which is able to learn human motion behavior
from demonstrated data. To the best of our knowledge, this is the first
approach using LSTMs, that incorporates both static obstacles and surrounding
pedestrians for trajectory forecasting. As part of the model, we introduce a
new way of encoding surrounding pedestrians based on a 1d-grid in polar angle
space. We evaluate the benefit of interaction-aware motion prediction and the
added value of incorporating static obstacles on both simulation and real-world
datasets by comparing with state-of-the-art approaches. The results show, that
our new approach outperforms the other approaches while being very
computationally efficient and that taking into account static obstacles for
motion predictions significantly improves the prediction accuracy, especially
in cluttered environments.Comment: 8 pages, accepted for publication at the IEEE International
Conference on Robotics and Automation (ICRA) 201
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