32,324 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
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
The THUMOS Challenge on Action Recognition for Videos "in the Wild"
Automatically recognizing and localizing wide ranges of human actions has
crucial importance for video understanding. Towards this goal, the THUMOS
challenge was introduced in 2013 to serve as a benchmark for action
recognition. Until then, video action recognition, including THUMOS challenge,
had focused primarily on the classification of pre-segmented (i.e., trimmed)
videos, which is an artificial task. In THUMOS 2014, we elevated action
recognition to a more practical level by introducing temporally untrimmed
videos. These also include `background videos' which share similar scenes and
backgrounds as action videos, but are devoid of the specific actions. The three
editions of the challenge organized in 2013--2015 have made THUMOS a common
benchmark for action classification and detection and the annual challenge is
widely attended by teams from around the world.
In this paper we describe the THUMOS benchmark in detail and give an overview
of data collection and annotation procedures. We present the evaluation
protocols used to quantify results in the two THUMOS tasks of action
classification and temporal detection. We also present results of submissions
to the THUMOS 2015 challenge and review the participating approaches.
Additionally, we include a comprehensive empirical study evaluating the
differences in action recognition between trimmed and untrimmed videos, and how
well methods trained on trimmed videos generalize to untrimmed videos. We
conclude by proposing several directions and improvements for future THUMOS
challenges.Comment: Preprint submitted to Computer Vision and Image Understandin
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