5,348 research outputs found
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
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
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
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