11,775 research outputs found
Learning Human Pose Estimation Features with Convolutional Networks
This paper introduces a new architecture for human pose estimation using a
multi- layer convolutional network architecture and a modified learning
technique that learns low-level features and higher-level weak spatial models.
Unconstrained human pose estimation is one of the hardest problems in computer
vision, and our new architecture and learning schema shows significant
improvement over the current state-of-the-art results. The main contribution of
this paper is showing, for the first time, that a specific variation of deep
learning is able to outperform all existing traditional architectures on this
task. The paper also discusses several lessons learned while researching
alternatives, most notably, that it is possible to learn strong low-level
feature detectors on features that might even just cover a few pixels in the
image. Higher-level spatial models improve somewhat the overall result, but to
a much lesser extent then expected. Many researchers previously argued that the
kinematic structure and top-down information is crucial for this domain, but
with our purely bottom up, and weak spatial model, we could improve other more
complicated architectures that currently produce the best results. This mirrors
what many other researchers, like those in the speech recognition, object
recognition, and other domains have experienced
Cascaded Scene Flow Prediction using Semantic Segmentation
Given two consecutive frames from a pair of stereo cameras, 3D scene flow
methods simultaneously estimate the 3D geometry and motion of the observed
scene. Many existing approaches use superpixels for regularization, but may
predict inconsistent shapes and motions inside rigidly moving objects. We
instead assume that scenes consist of foreground objects rigidly moving in
front of a static background, and use semantic cues to produce pixel-accurate
scene flow estimates. Our cascaded classification framework accurately models
3D scenes by iteratively refining semantic segmentation masks, stereo
correspondences, 3D rigid motion estimates, and optical flow fields. We
evaluate our method on the challenging KITTI autonomous driving benchmark, and
show that accounting for the motion of segmented vehicles leads to
state-of-the-art performance.Comment: International Conference on 3D Vision (3DV), 2017 (oral presentation
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