12,904 research outputs found
DeepPose: Human Pose Estimation via Deep Neural Networks
We propose a method for human pose estimation based on Deep Neural Networks
(DNNs). The pose estimation is formulated as a DNN-based regression problem
towards body joints. We present a cascade of such DNN regressors which results
in high precision pose estimates. The approach has the advantage of reasoning
about pose in a holistic fashion and has a simple but yet powerful formulation
which capitalizes on recent advances in Deep Learning. We present a detailed
empirical analysis with state-of-art or better performance on four academic
benchmarks of diverse real-world images.Comment: IEEE Conference on Computer Vision and Pattern Recognition, 201
Beyond Physical Connections: Tree Models in Human Pose Estimation
Simple tree models for articulated objects prevails in the last decade.
However, it is also believed that these simple tree models are not capable of
capturing large variations in many scenarios, such as human pose estimation.
This paper attempts to address three questions: 1) are simple tree models
sufficient? more specifically, 2) how to use tree models effectively in human
pose estimation? and 3) how shall we use combined parts together with single
parts efficiently?
Assuming we have a set of single parts and combined parts, and the goal is to
estimate a joint distribution of their locations. We surprisingly find that no
latent variables are introduced in the Leeds Sport Dataset (LSP) during
learning latent trees for deformable model, which aims at approximating the
joint distributions of body part locations using minimal tree structure. This
suggests one can straightforwardly use a mixed representation of single and
combined parts to approximate their joint distribution in a simple tree model.
As such, one only needs to build Visual Categories of the combined parts, and
then perform inference on the learned latent tree. Our method outperformed the
state of the art on the LSP, both in the scenarios when the training images are
from the same dataset and from the PARSE dataset. Experiments on animal images
from the VOC challenge further support our findings.Comment: CVPR 201
Combining Local Appearance and Holistic View: Dual-Source Deep Neural Networks for Human Pose Estimation
We propose a new learning-based method for estimating 2D human pose from a
single image, using Dual-Source Deep Convolutional Neural Networks (DS-CNN).
Recently, many methods have been developed to estimate human pose by using pose
priors that are estimated from physiologically inspired graphical models or
learned from a holistic perspective. In this paper, we propose to integrate
both the local (body) part appearance and the holistic view of each local part
for more accurate human pose estimation. Specifically, the proposed DS-CNN
takes a set of image patches (category-independent object proposals for
training and multi-scale sliding windows for testing) as the input and then
learns the appearance of each local part by considering their holistic views in
the full body. Using DS-CNN, we achieve both joint detection, which determines
whether an image patch contains a body joint, and joint localization, which
finds the exact location of the joint in the image patch. Finally, we develop
an algorithm to combine these joint detection/localization results from all the
image patches for estimating the human pose. The experimental results show the
effectiveness of the proposed method by comparing to the state-of-the-art
human-pose estimation methods based on pose priors that are estimated from
physiologically inspired graphical models or learned from a holistic
perspective.Comment: CVPR 201
LSTM Pose Machines
We observed that recent state-of-the-art results on single image human pose
estimation were achieved by multi-stage Convolution Neural Networks (CNN).
Notwithstanding the superior performance on static images, the application of
these models on videos is not only computationally intensive, it also suffers
from performance degeneration and flicking. Such suboptimal results are mainly
attributed to the inability of imposing sequential geometric consistency,
handling severe image quality degradation (e.g. motion blur and occlusion) as
well as the inability of capturing the temporal correlation among video frames.
In this paper, we proposed a novel recurrent network to tackle these problems.
We showed that if we were to impose the weight sharing scheme to the
multi-stage CNN, it could be re-written as a Recurrent Neural Network (RNN).
This property decouples the relationship among multiple network stages and
results in significantly faster speed in invoking the network for videos. It
also enables the adoption of Long Short-Term Memory (LSTM) units between video
frames. We found such memory augmented RNN is very effective in imposing
geometric consistency among frames. It also well handles input quality
degradation in videos while successfully stabilizes the sequential outputs. The
experiments showed that our approach significantly outperformed current
state-of-the-art methods on two large-scale video pose estimation benchmarks.
We also explored the memory cells inside the LSTM and provided insights on why
such mechanism would benefit the prediction for video-based pose estimations.Comment: Poster in IEEE Conference on Computer Vision and Pattern Recognition
(CVPR), 201
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