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

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

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 A vehicle re-identification framework based on the improved multi-branch feature fusion network

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Leilei Rong ◽  
Yan Xu ◽  
Xiaolei Zhou ◽  
Lisu Han ◽  
Linghui Li ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Intelligent Transportation System ◽  
Feature Fusion ◽  
State Of The Art ◽  
Local Feature ◽  
Learning Ability ◽  
Target Vehicle ◽  
Illumination Changes ◽  
Global And Local ◽  
Vehicle Movement

AbstractVehicle re-identification (re-id) aims to solve the problems of matching and identifying the same vehicle under the scenes across multiple surveillance cameras. For public security and intelligent transportation system (ITS), it is extremely important to locate the target vehicle quickly and accurately in the massive vehicle database. However, re-id of the target vehicle is very challenging due to many factors, such as the orientation variations, illumination changes, occlusion, low resolution, rapid vehicle movement, and amounts of similar vehicle models. In order to resolve the difficulties and enhance the accuracy for vehicle re-id, in this work, we propose an improved multi-branch network in which global–local feature fusion, channel attention mechanism and weighted local feature are comprehensively combined. Firstly, the fusion of global and local features is adopted to obtain more information of the vehicle and enhance the learning ability of the model; Secondly, the channel attention module in the feature extraction branch is embedded to extract the personalized features of the targeting vehicle; Finally, the background and noise information on feature extraction is controlled by weighted local feature. The results of comprehensive experiments on the mainstream evaluation datasets including VeRi-776, VRIC, and VehicleID indicate that our method can effectively improve the accuracy of vehicle re-identification and is superior to the state-of-the-art methods.

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