scholarly journals Hand-Crafted Features vs Deep Learning for Pedestrian Detection in Moving Camera

2020 ◽  
Vol 37 (2) ◽  
pp. 209-216
Author(s):  
Bilel Tarchoun ◽  
Anouar Khalifa ◽  
Selma Dhifallah ◽  
Imen Jegham ◽  
Mohamed Mahjou
Author(s):  
Utkarsha Sagar ◽  
Ravi Raja ◽  
Himanshu Shekhar

2016 ◽  
Vol 14 (1) ◽  
pp. 172988141769231 ◽  
Author(s):  
Yingfeng Cai ◽  
Youguo He ◽  
Hai Wang ◽  
Xiaoqiang Sun ◽  
Long Chen ◽  
...  

The emergence and development of deep learning theory in machine learning field provide new method for visual-based pedestrian recognition technology. To achieve better performance in this application, an improved weakly supervised hierarchical deep learning pedestrian recognition algorithm with two-dimensional deep belief networks is proposed. The improvements are made by taking into consideration the weaknesses of structure and training methods of existing classifiers. First, traditional one-dimensional deep belief network is expanded to two-dimensional that allows image matrix to be loaded directly to preserve more information of a sample space. Then, a determination regularization term with small weight is added to the traditional unsupervised training objective function. By this modification, original unsupervised training is transformed to weakly supervised training. Subsequently, that gives the extracted features discrimination ability. Multiple sets of comparative experiments show that the performance of the proposed algorithm is better than other deep learning algorithms in recognition rate and outperforms most of the existing state-of-the-art methods in non-occlusion pedestrian data set while performs fair in weakly and heavily occlusion data set.


Author(s):  
Runxing Chen ◽  
Xiaofei Wang ◽  
Yong Liu ◽  
Sen Wang ◽  
Shuo Huang

2018 ◽  
Vol 55 (11) ◽  
pp. 111007 ◽  
Author(s):  
田青 Tian Qing ◽  
袁曈阳 Yuan Tongyang ◽  
杨丹 Yang Dan ◽  
魏运 Wei Yun

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