Exploiting Local Feature Fusion for Action Recognition

2016 ◽  
pp. 221-230
Author(s):  
Jie Miao ◽  
Xiangmin Xu ◽  
Xiaoyi Jia ◽  
Haoyu Huang ◽  
Bolun Cai ◽  
...  
Keyword(s):  
Action Recognition ◽  
Feature Fusion ◽  
Local Feature

scholarly journals Vehicle re-identification method based on global and local feature fusion

2021 ◽  
Vol 2005 (1) ◽  
pp. 012059
Author(s):  
Zhou Bing ◽  
Wei Wei ◽  
Wu Huaihong
Keyword(s):  
Feature Fusion ◽  
Local Feature ◽  
Identification Method ◽  
Global And Local

Lightweight feature fusion network design for local feature recognition of non-cooperative target

2020 ◽  
Vol 49 (7) ◽  
pp. 20200170
Author(s):  
徐云飞 Yunfei Xu ◽  
张笃周 Duzhou Zhang ◽  
王立 Li Wang ◽  
华宝成 Baocheng Hua
Keyword(s):  
Network Design ◽  
Feature Recognition ◽  
Feature Fusion ◽  
Local Feature

scholarly journals Using a Multilearner to Fuse Multimodal Features for Human Action Recognition

2020 ◽  
Vol 2020 ◽  
pp. 1-18
Author(s):  
Chao Tang ◽  
Huosheng Hu ◽  
Wenjian Wang ◽  
Wei Li ◽  
Hua Peng ◽  
...  
Keyword(s):  
Action Recognition ◽  
Feature Fusion ◽  
Human Action Recognition ◽  
Human Action ◽  
Human Motion ◽  
Image Features ◽  
Depth Image ◽  
Good Ability ◽  
Multimodal Features ◽  
Feature Based

The representation and selection of action features directly affect the recognition effect of human action recognition methods. Single feature is often affected by human appearance, environment, camera settings, and other factors. Aiming at the problem that the existing multimodal feature fusion methods cannot effectively measure the contribution of different features, this paper proposed a human action recognition method based on RGB-D image features, which makes full use of the multimodal information provided by RGB-D sensors to extract effective human action features. In this paper, three kinds of human action features with different modal information are proposed: RGB-HOG feature based on RGB image information, which has good geometric scale invariance; D-STIP feature based on depth image, which maintains the dynamic characteristics of human motion and has local invariance; and S-JRPF feature-based skeleton information, which has good ability to describe motion space structure. At the same time, multiple K-nearest neighbor classifiers with better generalization ability are used to integrate decision-making classification. The experimental results show that the algorithm achieves ideal recognition results on the public G3D and CAD60 datasets.

Multi-feature Fusion Action Recognition Based on Key Frames

2019 ◽  
Author(s):  
Yuerong Zhao ◽  
Ling Gao ◽  
Dan He ◽  
Hongbo Guo ◽  
Hai Wang ◽  
...  
Keyword(s):  
Action Recognition ◽  
Feature Fusion ◽  
Key Frames
Sign in / Sign up

Export Citation Format

Share Document