Open AccessPosted Content
DeeperCut: A Deeper, Stronger, and Faster Multi-Person Pose Estimation Model
Eldar Insafutdinov,Leonid Pishchulin,Bjoern Andres,Mykhaylo Andriluka,Mykhaylo Andriluka,Bernt Schiele +5 more
TL;DR: In this paper, an incremental optimization strategy was proposed to explore the search space more efficiently, leading both to better performance and significant speed-up factors for multi-person pose estimation.
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Abstract: The goal of this paper is to advance the state-of-the-art of articulated pose estimation in scenes with multiple people. To that end we contribute on three fronts. We propose (1) improved body part detectors that generate effective bottom-up proposals for body parts; (2) novel image-conditioned pairwise terms that allow to assemble the proposals into a variable number of consistent body part configurations; and (3) an incremental optimization strategy that explores the search space more efficiently thus leading both to better performance and significant speed-up factors. Evaluation is done on two single-person and two multi-person pose estimation benchmarks. The proposed approach significantly outperforms best known multi-person pose estimation results while demonstrating competitive performance on the task of single person pose estimation. Models and code available at this http URL
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Citations
QuickPose: Real-time Multi-view Multi-person Pose Estimation in Crowded Scenes
Zhize Zhou,Qing Shuai,Yize Wang,Qihang Fang,Xiaopeng Ji,Fashuai Li,Hujun Bao,Xiaowei Zhou +7 more
- 27 Jul 2022
TL;DR: This work forms the multi-view matching problem as mode seeking in the space of skeleton proposals and develops an efficient algorithm named QuickPose to solve the problem, which enables real-time motion capture in crowded scenes.
9
Conditional progressive network for clothing parsing
TL;DR: The authors propose a Conditional Progressive Network to parse clothing in different scales and prevent the mutual interference among labels and demonstrate their solution in parsing the fashion clothing cases on the ATR and the Fashion dataset.
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3D Pose Estimation and Tracking in Handball Actions Using a Monocular Camera
Romeo Sajina,Marina Ivašić-Kos +1 more
TL;DR: In this article , a 2D pose of the player is determined in each video frame, and converted into a 3D pose, then using the tracking method all the player poses are grouped into a sequence to construct a series of elements of a particular action.
Environment Upgrade Reinforcement Learning for Non-differentiable Multi-stage Pipelines
Shuqin Xie,Zitian Chen,Chao Xu,Cewu Lu +3 more
- 01 Jun 2018
TL;DR: This paper proposes a novel environment upgrade reinforcement learning framework that re-links the downstream stage to the upstream stage by a reinforcement learning agent, and upgrades the downstreamstage (environment) according to the agent's policy.
Skeleton estimation and tracking by means of depth data fusion from depth camera networks
TL;DR: An approach for estimation and tracking of the skeleton of the human body from camera networks exploiting only depth data taking advantage of multiple views by building and merging together the 3D point clouds is described.
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References
•Posted Content
Deep Residual Learning for Image Recognition
TL;DR: This work presents a residual learning framework to ease the training of networks that are substantially deeper than those used previously, and provides comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth.
117.9K
•Proceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
- 04 Sep 2014
TL;DR: This work investigates the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting using an architecture with very small convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers.
102.6K
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
TL;DR: This work introduces a Region Proposal Network (RPN) that shares full-image convolutional features with the detection network, thus enabling nearly cost-free region proposals and further merge RPN and Fast R-CNN into a single network by sharing their convolutionAL features.
Going deeper with convolutions
Christian Szegedy,Wei Liu,Yangqing Jia,Pierre Sermanet,Scott Reed,Dragomir Anguelov,Dumitru Erhan,Vincent Vanhoucke,Andrew Rabinovich +8 more
- 07 Jun 2015
TL;DR: Inception as mentioned in this paper is a deep convolutional neural network architecture that achieves the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14).
Fully convolutional networks for semantic segmentation
Jonathan Long,Evan Shelhamer,Trevor Darrell +2 more
- 07 Jun 2015
TL;DR: The key insight is to build “fully convolutional” networks that take input of arbitrary size and produce correspondingly-sized output with efficient inference and learning.
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