3,927 research outputs found
Detect-and-Track: Efficient Pose Estimation in Videos
This paper addresses the problem of estimating and tracking human body
keypoints in complex, multi-person video. We propose an extremely lightweight
yet highly effective approach that builds upon the latest advancements in human
detection and video understanding. Our method operates in two-stages: keypoint
estimation in frames or short clips, followed by lightweight tracking to
generate keypoint predictions linked over the entire video. For frame-level
pose estimation we experiment with Mask R-CNN, as well as our own proposed 3D
extension of this model, which leverages temporal information over small clips
to generate more robust frame predictions. We conduct extensive ablative
experiments on the newly released multi-person video pose estimation benchmark,
PoseTrack, to validate various design choices of our model. Our approach
achieves an accuracy of 55.2% on the validation and 51.8% on the test set using
the Multi-Object Tracking Accuracy (MOTA) metric, and achieves state of the art
performance on the ICCV 2017 PoseTrack keypoint tracking challenge.Comment: In CVPR 2018. Ranked first in ICCV 2017 PoseTrack challenge (keypoint
tracking in videos). Code: https://github.com/facebookresearch/DetectAndTrack
and webpage: https://rohitgirdhar.github.io/DetectAndTrack
Don't Forget The Past: Recurrent Depth Estimation from Monocular Video
Autonomous cars need continuously updated depth information. Thus far, depth
is mostly estimated independently for a single frame at a time, even if the
method starts from video input. Our method produces a time series of depth
maps, which makes it an ideal candidate for online learning approaches. In
particular, we put three different types of depth estimation (supervised depth
prediction, self-supervised depth prediction, and self-supervised depth
completion) into a common framework. We integrate the corresponding networks
with a ConvLSTM such that the spatiotemporal structures of depth across frames
can be exploited to yield a more accurate depth estimation. Our method is
flexible. It can be applied to monocular videos only or be combined with
different types of sparse depth patterns. We carefully study the architecture
of the recurrent network and its training strategy. We are first to
successfully exploit recurrent networks for real-time self-supervised monocular
depth estimation and completion. Extensive experiments show that our recurrent
method outperforms its image-based counterpart consistently and significantly
in both self-supervised scenarios. It also outperforms previous depth
estimation methods of the three popular groups. Please refer to
https://www.trace.ethz.ch/publications/2020/rec_depth_estimation/ for details.Comment: Please refer to our webpage for details
https://www.trace.ethz.ch/publications/2020/rec_depth_estimation
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