Proceedings Article10.1109/AEMCSE55572.2022.00104
Emergency Rescue Action Recognition Algorithm Based on Recurrent -Adaptive Graph Convolutional Networks
Zhi Hu,Zhiguo Shi +1 more
- 01 Apr 2022
pp 501-508
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.
read more
Abstract: In the context of emergency rescue training, aiming at the scene of action recognition and effect evaluation by video. In order to solve the problems of large consumption of existing methods and poor real-time detection effect, this paper uses the technology stack of lightweight OpenPose and Recurrent-Adaptive Graph Convolutional Networks (R-AGCN). The recognition accuracy is improved by introducing cyclic enhancement module and adaptive module, and it has certain advantages for continuous video recognition. Firstly, the first ten layers of VGG - 19 network are used to extract image features, and OpenPose is used to extract bone feature key point coordinates. R-AGCN is used to realize action recognition. The module enhances the influence of spatial dimension on temporal dimension and then improve recognition accuracy. Under the two evaluation criteria of NTU-RGB + D data set, the accuracy of this algorithm is 84.3 % and 94.9 %, respectively, and it also has good recognition effect in the actual scene.
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
References
•Proceedings Article
Mask R-CNN
Kaiming He,Georgia Gkioxari,Piotr Dollár,Ross Girshick +3 more
- 20 Mar 2017
TL;DR: This work presents a conceptually simple, flexible, and general framework for object instance segmentation that outperforms all existing, single-model entries on every task, including the COCO 2016 challenge winners.
19.7K
Backpropagation applied to handwritten zip code recognition
Yann LeCun,Bernhard E. Boser,John S. Denker,D. Henderson,Richard Howard,W. Hubbard,Lawrence D. Jackel +6 more
TL;DR: This paper demonstrates how constraints from the task domain can be integrated into a backpropagation network through the architecture of the network, successfully applied to the recognition of handwritten zip code digits provided by the U.S. Postal Service.
12.5K
OpenPose: Realtime Multi-Person 2D Pose Estimation Using Part Affinity Fields
TL;DR: OpenPose as mentioned in this paper uses Part Affinity Fields (PAFs) to learn to associate body parts with individuals in the image, which achieves high accuracy and real-time performance.
5.3K
•Posted Content
Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields
TL;DR: This work presents an approach to efficiently detect the 2D pose of multiple people in an image using a nonparametric representation, which it refers to as Part Affinity Fields (PAFs), to learn to associate body parts with individuals in the image.
5.2K
•Proceedings Article
Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
Sijie Yan,Yuanjun Xiong,Dahua Lin +2 more
- 27 Apr 2018
TL;DR: Wang et al. as discussed by the authors proposed a novel model of dynamic skeletons called Spatial-Temporal Graph Convolutional Networks (ST-GCN), which moves beyond the limitations of previous methods by automatically learning both the spatial and temporal patterns from data.