Existing methods for pedestrian motion trajectory prediction are learning and predicting the trajectories in the 2D image space. In this work, we observe that it is much more efficient to learn and predict pedestrian trajectories in the 3D space since the human motion occurs in the 3D physical world and and their behavior patterns are better represented in the 3D space. To this end, we use a stereo camera system to detect and track the human pose with deep neural networks. During pose estimation, these twin deep neural networks satisfy the stereo consistence constraint. We adapt the existing SocialGAN method to perform pedestrian motion trajectory prediction from the 2D to the 3D space. Our extensive experimental results demonstrate that our proposed method significantly improves the pedestrian trajectory prediction performance, outperforming existing state-of-the-art methods.
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Learning Pose Grammar to Encode Human Body Configuration for 3D Pose Estimation
In this paper, we propose a pose grammar to tackle the problem
of 3D human pose estimation. Our model directly takes
2D pose as input and learns a generalized 2D-3D mapping
function. The proposed model consists of a base network
which efficiently captures pose-aligned features and a hierarchy
of Bi-directional RNNs (BRNN) on the top to explicitly
incorporate a set of knowledge regarding human body configuration
(i.e., kinematics, symmetry, motor coordination). The
proposed model thus enforces high-level constraints over human
poses. In learning, we develop a pose sample simulator
to augment training samples in virtual camera views, which
further improves our model generalizability. We validate our
method on public 3D human pose benchmarks and propose a
new evaluation protocol working on cross-view setting to verify
the generalization capability of different methods.We empirically
observe that most state-of-the-art methods encounter
difficulty under such setting while our method can well handle
such challenges.
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- Award ID(s):
- 1657600
- NSF-PAR ID:
- 10056961
- Date Published:
- Journal Name:
- AAAI Conference on Artificial Intelligence
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
