111,854 research outputs found
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
HandyPose and VehiPose: Pose Estimation of Flexible and Rigid Objects
Pose estimation is an important and challenging task in computer vision. Hand pose estimation has drawn increasing attention during the past decade and has been utilized in a wide range of applications including augmented reality, virtual reality, human-computer interaction, and action recognition. Hand pose is more challenging than general human body pose estimation due to the large number of degrees of freedom and the frequent occlusions of joints. To address these challenges, we propose HandyPose, a single-pass, end-to-end trainable architecture for hand pose estimation. Adopting an encoder-decoder framework with multi-level features, our method achieves high accuracy in hand pose while maintaining manageable size complexity and modularity of the network. HandyPose takes a multi-scale approach to representing context by incorporating spatial information at various levels of the network to mitigate the loss of resolution due to pooling. Our advanced multi-level waterfall architecture leverages the efficiency of progressive cascade filtering while maintaining larger fields-of-view through the concatenation of multi-level features from different levels of the network in the waterfall module. The decoder incorporates both the waterfall and multi-scale features for the generation of accurate joint heatmaps in a single stage. Recent developments in computer vision and deep learning have achieved significant progress in human pose estimation, but little of this work has been applied to vehicle pose. We also propose VehiPose, an efficient architecture for vehicle pose estimation, based on a multi-scale deep learning approach that achieves high accuracy vehicle pose estimation while maintaining manageable network complexity and modularity. The VehiPose architecture combines an encoder-decoder architecture with a waterfall atrous convolution module for multi-scale feature representation. It incorporates contextual information across scales and performs the localization of vehicle keypoints in an end-to-end trainable network. Our HandyPose architecture has a baseline of vehipose with an improvement in performance by incorporating multi-level features from different levels of the backbone and introducing novel multi-level modules. HandyPose and VehiPose more thoroughly leverage the image contextual information and deal with the issue of spatial loss of resolution due to successive pooling while maintaining the size complexity, modularity of the network, and preserve the spatial information at various levels of the network. Our results demonstrate state-of-the-art performance on popular datasets and show that HandyPose and VehiPose are robust and efficient architectures for hand and vehicle pose estimation
Global Relation Modeling and Refinement for Bottom-Up Human Pose Estimation
In this paper, we concern on the bottom-up paradigm in multi-person pose
estimation (MPPE). Most previous bottom-up methods try to consider the relation
of instances to identify different body parts during the post processing, while
ignoring to model the relation among instances or environment in the feature
learning process. In addition, most existing works adopt the operations of
upsampling and downsampling. During the sampling process, there will be a
problem of misalignment with the source features, resulting in deviations in
the keypoint features learned by the model.
To overcome the above limitations, we propose a convolutional neural network
for bottom-up human pose estimation. It invovles two basic modules: (i) Global
Relation Modeling (GRM) module globally learns relation (e.g., environment
context, instance interactive information) among region of image by fusing
multiple stages features in the feature learning process. It combines with the
spatial-channel attention mechanism, which focuses on achieving adaptability in
spatial and channel dimensions. (ii) Multi-branch Feature Align (MFA) module
aggregates features from multiple branches to align fused feature and obtain
refined local keypoint representation. Our model has the ability to focus on
different granularity from local to global regions, which significantly boosts
the performance of the multi-person pose estimation. Our results on the COCO
and CrowdPose datasets demonstrate that it is an efficient framework for
multi-person pose estimation
Joint Multi-Person Pose Estimation and Semantic Part Segmentation
Human pose estimation and semantic part segmentation are two complementary
tasks in computer vision. In this paper, we propose to solve the two tasks
jointly for natural multi-person images, in which the estimated pose provides
object-level shape prior to regularize part segments while the part-level
segments constrain the variation of pose locations. Specifically, we first
train two fully convolutional neural networks (FCNs), namely Pose FCN and Part
FCN, to provide initial estimation of pose joint potential and semantic part
potential. Then, to refine pose joint location, the two types of potentials are
fused with a fully-connected conditional random field (FCRF), where a novel
segment-joint smoothness term is used to encourage semantic and spatial
consistency between parts and joints. To refine part segments, the refined pose
and the original part potential are integrated through a Part FCN, where the
skeleton feature from pose serves as additional regularization cues for part
segments. Finally, to reduce the complexity of the FCRF, we induce human
detection boxes and infer the graph inside each box, making the inference forty
times faster.
Since there's no dataset that contains both part segments and pose labels, we
extend the PASCAL VOC part dataset with human pose joints and perform extensive
experiments to compare our method against several most recent strategies. We
show that on this dataset our algorithm surpasses competing methods by a large
margin in both tasks.Comment: This paper has been accepted by CVPR 201
Pedestrian Attribute Recognition: A Survey
Recognizing pedestrian attributes is an important task in computer vision
community due to it plays an important role in video surveillance. Many
algorithms has been proposed to handle this task. The goal of this paper is to
review existing works using traditional methods or based on deep learning
networks. Firstly, we introduce the background of pedestrian attributes
recognition (PAR, for short), including the fundamental concepts of pedestrian
attributes and corresponding challenges. Secondly, we introduce existing
benchmarks, including popular datasets and evaluation criterion. Thirdly, we
analyse the concept of multi-task learning and multi-label learning, and also
explain the relations between these two learning algorithms and pedestrian
attribute recognition. We also review some popular network architectures which
have widely applied in the deep learning community. Fourthly, we analyse
popular solutions for this task, such as attributes group, part-based,
\emph{etc}. Fifthly, we shown some applications which takes pedestrian
attributes into consideration and achieve better performance. Finally, we
summarized this paper and give several possible research directions for
pedestrian attributes recognition. The project page of this paper can be found
from the following website:
\url{https://sites.google.com/view/ahu-pedestrianattributes/}.Comment: Check our project page for High Resolution version of this survey:
https://sites.google.com/view/ahu-pedestrianattributes
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