scholarly journals Pedestrian Detection in Far-Infrared Daytime Images Using a Hierarchical Codebook of SURF

Sensors ◽  
2015 ◽  
Vol 15 (4) ◽  
pp. 8570-8594 ◽  
Author(s):  
Bassem Besbes ◽  
Alexandrina Rogozan ◽  
Adela-Maria Rus ◽  
Abdelaziz Bensrhair ◽  
Alberto Broggi
2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Zhaoli Wu ◽  
Xin Wang ◽  
Chao Chen

Due to the limitation of energy consumption and power consumption, the embedded platform cannot meet the real-time requirements of the far-infrared image pedestrian detection algorithm. To solve this problem, this paper proposes a new real-time infrared pedestrian detection algorithm (RepVGG-YOLOv4, Rep-YOLO), which uses RepVGG to reconstruct the YOLOv4 backbone network, reduces the amount of model parameters and calculations, and improves the speed of target detection; using space spatial pyramid pooling (SPP) obtains different receptive field information to improve the accuracy of model detection; using the channel pruning compression method reduces redundant parameters, model size, and computational complexity. The experimental results show that compared with the YOLOv4 target detection algorithm, the Rep-YOLO algorithm reduces the model volume by 90%, the floating-point calculation is reduced by 93.4%, the reasoning speed is increased by 4 times, and the model detection accuracy after compression reaches 93.25%.


Author(s):  
Emmanuel Bercier ◽  
Patrick Robert ◽  
David Pochic ◽  
Jean-Luc Tissot ◽  
Agnes Arnaud ◽  
...  

Author(s):  
Massimo Bertozzi ◽  
Alberto Broggi ◽  
Mirko Felisa ◽  
Stefano Ghidoni ◽  
Paolo Grisleri ◽  
...  

2013 ◽  
Vol 20 (4) ◽  
pp. 347-360 ◽  
Author(s):  
Daniel Olmeda ◽  
Cristiano Premebida ◽  
Urbano Nunes ◽  
Jose Maria Armingol ◽  
Arturo de la Escalera

Sign in / Sign up

Export Citation Format

Share Document