13,605 research outputs found
Articulated Clinician Detection Using 3D Pictorial Structures on RGB-D Data
Reliable human pose estimation (HPE) is essential to many clinical
applications, such as surgical workflow analysis, radiation safety monitoring
and human-robot cooperation. Proposed methods for the operating room (OR) rely
either on foreground estimation using a multi-camera system, which is a
challenge in real ORs due to color similarities and frequent illumination
changes, or on wearable sensors or markers, which are invasive and therefore
difficult to introduce in the room. Instead, we propose a novel approach based
on Pictorial Structures (PS) and on RGB-D data, which can be easily deployed in
real ORs. We extend the PS framework in two ways. First, we build robust and
discriminative part detectors using both color and depth images. We also
present a novel descriptor for depth images, called histogram of depth
differences (HDD). Second, we extend PS to 3D by proposing 3D pairwise
constraints and a new method that makes exact inference tractable. Our approach
is evaluated for pose estimation and clinician detection on a challenging RGB-D
dataset recorded in a busy operating room during live surgeries. We conduct
series of experiments to study the different part detectors in conjunction with
the various 2D or 3D pairwise constraints. Our comparisons demonstrate that 3D
PS with RGB-D part detectors significantly improves the results in a visually
challenging operating environment.Comment: The supplementary video is available at https://youtu.be/iabbGSqRSg
Occlusion Coherence: Detecting and Localizing Occluded Faces
The presence of occluders significantly impacts object recognition accuracy.
However, occlusion is typically treated as an unstructured source of noise and
explicit models for occluders have lagged behind those for object appearance
and shape. In this paper we describe a hierarchical deformable part model for
face detection and landmark localization that explicitly models part occlusion.
The proposed model structure makes it possible to augment positive training
data with large numbers of synthetically occluded instances. This allows us to
easily incorporate the statistics of occlusion patterns in a discriminatively
trained model. We test the model on several benchmarks for landmark
localization and detection including challenging new data sets featuring
significant occlusion. We find that the addition of an explicit occlusion model
yields a detection system that outperforms existing approaches for occluded
instances while maintaining competitive accuracy in detection and landmark
localization for unoccluded instances
Parsing Occluded People by Flexible Compositions
This paper presents an approach to parsing humans when there is significant
occlusion. We model humans using a graphical model which has a tree structure
building on recent work [32, 6] and exploit the connectivity prior that, even
in presence of occlusion, the visible nodes form a connected subtree of the
graphical model. We call each connected subtree a flexible composition of
object parts. This involves a novel method for learning occlusion cues. During
inference we need to search over a mixture of different flexible models. By
exploiting part sharing, we show that this inference can be done extremely
efficiently requiring only twice as many computations as searching for the
entire object (i.e., not modeling occlusion). We evaluate our model on the
standard benchmarked "We Are Family" Stickmen dataset and obtain significant
performance improvements over the best alternative algorithms.Comment: CVPR 15 Camera Read
Heterogeneous Multi-task Learning for Human Pose Estimation with Deep Convolutional Neural Network
We propose an heterogeneous multi-task learning framework for human pose
estimation from monocular image with deep convolutional neural network. In
particular, we simultaneously learn a pose-joint regressor and a sliding-window
body-part detector in a deep network architecture. We show that including the
body-part detection task helps to regularize the network, directing it to
converge to a good solution. We report competitive and state-of-art results on
several data sets. We also empirically show that the learned neurons in the
middle layer of our network are tuned to localized body parts
Human Pose Estimation using Global and Local Normalization
In this paper, we address the problem of estimating the positions of human
joints, i.e., articulated pose estimation. Recent state-of-the-art solutions
model two key issues, joint detection and spatial configuration refinement,
together using convolutional neural networks. Our work mainly focuses on
spatial configuration refinement by reducing variations of human poses
statistically, which is motivated by the observation that the scattered
distribution of the relative locations of joints e.g., the left wrist is
distributed nearly uniformly in a circular area around the left shoulder) makes
the learning of convolutional spatial models hard. We present a two-stage
normalization scheme, human body normalization and limb normalization, to make
the distribution of the relative joint locations compact, resulting in easier
learning of convolutional spatial models and more accurate pose estimation. In
addition, our empirical results show that incorporating multi-scale supervision
and multi-scale fusion into the joint detection network is beneficial.
Experiment results demonstrate that our method consistently outperforms
state-of-the-art methods on the benchmarks.Comment: ICCV201
Multi-Context Attention for Human Pose Estimation
In this paper, we propose to incorporate convolutional neural networks with a
multi-context attention mechanism into an end-to-end framework for human pose
estimation. We adopt stacked hourglass networks to generate attention maps from
features at multiple resolutions with various semantics. The Conditional Random
Field (CRF) is utilized to model the correlations among neighboring regions in
the attention map. We further combine the holistic attention model, which
focuses on the global consistency of the full human body, and the body part
attention model, which focuses on the detailed description for different body
parts. Hence our model has the ability to focus on different granularity from
local salient regions to global semantic-consistent spaces. Additionally, we
design novel Hourglass Residual Units (HRUs) to increase the receptive field of
the network. These units are extensions of residual units with a side branch
incorporating filters with larger receptive fields, hence features with various
scales are learned and combined within the HRUs. The effectiveness of the
proposed multi-context attention mechanism and the hourglass residual units is
evaluated on two widely used human pose estimation benchmarks. Our approach
outperforms all existing methods on both benchmarks over all the body parts.Comment: The first two authors contribute equally to this wor
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