A novel multi-feature fusion method for tracking based on discriminative power of feature

2011 ◽  
Author(s):  
Jing Qi ◽  
Danpei Zhao ◽  
Zhenhua Su
Keyword(s):  
Feature Fusion ◽  
Fusion Method ◽  
Discriminative Power

scholarly journals A Shallow–Deep Feature Fusion Method for Pedestrian Detection

2021 ◽  
Vol 11 (19) ◽  
pp. 9202
Author(s):  
Daxue Liu ◽  
Kai Zang ◽  
Jifeng Shen
Keyword(s):  
Detection Method ◽  
Feature Fusion ◽  
Pedestrian Detection ◽  
Recall Rate ◽  
Fusion Method ◽  
Discriminative Power ◽  
Deep Feature ◽  
Proposal A ◽  
High Recall Rate ◽  
Feature Based

In this paper, a shallow–deep feature fusion (SDFF) method is developed for pedestrian detection. Firstly, we propose a shallow feature-based method under the ACF framework of pedestrian detection. More precisely, improved Haar-like templates with Local FDA learning are used to filter the channel maps of ACF such that these Haar-like features are able to improve the discriminative power and therefore enhance the detection performance. The proposed shallow feature is also referred to as weighted subset-haar-like feature. It is efficient in pedestrian detection with a high recall rate and precise localization. Secondly, the proposed shallow feature-based detection method operates as a region proposal. A classifier equipped with ResNet is then used to refine the region proposals to judge whether each region contains a pedestrian or not. The extensive experiments evaluated on INRIA, Caltech, and TUD-Brussel datasets show that SDFF is an effective and efficient method for pedestrian detection.

A new Feature-Fusion method based on training dataset prototype for surface defect recognition

2021 ◽  
Vol 50 ◽  
pp. 101392
Author(s):  
Yucheng Wang ◽  
Xinyu Li ◽  
Yiping Gao ◽  
Lijian Wang ◽  
Liang Gao
Keyword(s):  
Surface Defect ◽  
Feature Fusion ◽  
Training Dataset ◽  
Fusion Method ◽  
New Feature ◽  
Defect Recognition
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