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.
read more
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
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
•Posted Content
A Global to Local Double Embedding Method for Multi-person Pose Estimation
TL;DR: Zhang et al. as discussed by the authors proposed a double embedding (DE) method to complete the multi-person pose estimation task in a global-to-local way, which consists of global embedding and local embedding.
Emergency Rescue Action Recognition Algorithm Based on Recurrent -Adaptive Graph Convolutional Networks
Zhi Hu,Zhiguo Shi +1 more
- 01 Apr 2022
TL;DR: Wang et al. as mentioned in this paper used the technology stack of lightweight OpenPose and Recurrent-Adaptive Graph Convolutional Networks (R-AGCN) for scene of action recognition and effect evaluation by video.
Proceedings of the 1st Workshop on Proximity Perception in Robotics at IROS 2018, Madrid, Spain
Stefan Escaida Navarro,Stephan Mühlbacher-Karrer,Hosam Alagi,Björn Hein,Hubert Zangl +4 more
- 01 Jan 2018
TL;DR: This work presents a framework that realises separation distance monitoring between a robot and a human operator based on key point pair-wise evaluation and shows preliminary results using a Nao humanoid robot and an RealSense RGBD sensor and employing OpenPose human skeleton estimation algorithm.
Branch Information Correction Network for Human Pose Estimation
Qingzhan Ni,Chenxing Wang,Feipeng Da +2 more
- 16 Oct 2020
TL;DR: In this paper, a branch information correction network is proposed to make full use of the complementary information on branches to improve the accuracy of the keypoint prediction of the human body, and can correct some key points which are easily affected by the environment.
Event prediction via spatio-temporal sequence analysis
ZeXian Li,Tian Wang,Yi Yang,Yan Wang,Peng Shi,Hichem Snoussi +5 more
- 01 Nov 2019
TL;DR: The means of deep learning is proposed to conduct research on sequence images’ event prediction in time and space dimensions to solve the problem of missing semantic information and lack of external information in the current event prediction research.
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.
Related Papers (5)
Kaiming He,Xiangyu Zhang,Shaoqing Ren,Jian Sun +3 more
- 27 Jun 2016
Alexander Toshev,Christian Szegedy +1 more
- 23 Jun 2014