Pedestrian detection using a moving camera: A novel framework for foreground detection

2020 ◽  
Vol 60 ◽  
pp. 77-96 ◽  
Author(s):  
Anouar Ben Khalifa ◽  
Ihsen Alouani ◽  
Mohamed Ali Mahjoub ◽  
Najoua Essoukri Ben Amara
Keyword(s):  
Pedestrian Detection ◽  
Foreground Detection ◽  
Moving Camera

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
Keyword(s):  
Deep Learning ◽  
Pedestrian Detection ◽  
Moving Camera

Evaluation of foreground detection methodology for a moving camera

2015 ◽  
Author(s):  
Tsubasa Minematsu ◽  
Hideaki Uchiyama ◽  
Atsushi Shimada ◽  
Hajime Nagahara ◽  
Rin-ichiro Taniguchi
Keyword(s):  
Foreground Detection ◽  
Moving Camera

scholarly journals Pedestrian Motion Path Detection Method Based on Deep Learning and Foreground Detection

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Meiman Li ◽  
Wenfu Xie
Keyword(s):  
Deep Learning ◽  
Pedestrian Detection ◽  
Detection Algorithm ◽  
Motion Path ◽  
Support Vector ◽  
Foreground Detection ◽  
Surveillance Video ◽  
Detection Range ◽  
Window Method ◽  
Fusion Detection

For the surveillance video images captured by monocular camera, this paper proposes a method combining foreground detection and deep learning to detect moving pedestrians, making full use of the invariable background of video image. Firstly, the motion region is extracted by the method of interframe difference and background difference. Then, the normalized motion region extracts the feature vectors based on the improved YOLOv3 tiny network. Finally, the trained linear support vector machine is used for pedestrian detection, and the performance of the fusion detection algorithm on caviar dataset is given, which proves the effectiveness of the proposed fusion detection algorithm. Experimental results show that the proposed method not only improves the practical application of pedestrian rerecognition but also reduces the detection range, computational complexity, and false detection rate compared with sliding window method.

Deep Learning for Pedestrian Detection

2019 ◽  
Vol 9 (8) ◽  
pp. p9212
Author(s):  
Utkarsha Sagar ◽  
Ravi Raja ◽  
Himanshu Shekhar
Keyword(s):  
Deep Learning ◽  
Pedestrian Detection
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