An Efficient Region of Interest Generation Technique for Far-Infrared Pedestrian Detection

2008 ◽  
Author(s):  
Ronan O'Malley ◽  
Martin Glavin ◽  
Edward Jones
Keyword(s):  
Pedestrian Detection ◽  
Region Of Interest ◽  
Far Infrared ◽  
Generation Technique

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
Keyword(s):  
Pedestrian Detection ◽  
Far Infrared

Pedestrian Detection Based on Histograms of Oriented Gradients in ROI

2012 ◽  
Vol 542-543 ◽  
pp. 937-940
Author(s):  
Ping Shu Ge ◽  
Guo Kai Xu ◽  
Xiu Chun Zhao ◽  
Peng Song ◽  
Lie Guo
Keyword(s):  
Traditional Method ◽  
Detection Method ◽  
Feature Vector ◽  
Pedestrian Detection ◽  
Region Of Interest ◽  
Experimental Results ◽  
Process Time ◽  
Vertical Edge ◽  
Histograms Of Oriented Gradients

To locate pedestrian faster and more accurately, a pedestrian detection method based on histograms of oriented gradients (HOG) in region of interest (ROI) is introduced. The features are extracted in the ROI where the pedestrian's legs may exist, which is helpful to decrease the dimension of feature vector and simplify the calculation. Then the vertical edge symmetry of pedestrian's legs is fused to confirm the detection. Experimental results indicate that this method can achieve an ideal accuracy with lower process time compared to traditional method.

scholarly journals Research on Lightweight Infrared Pedestrian Detection Model Algorithm for Embedded Platform

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Zhaoli Wu ◽  
Xin Wang ◽  
Chao Chen
Keyword(s):  
Real Time ◽  
Target Detection ◽  
Pedestrian Detection ◽  
Infrared Image ◽  
Far Infrared ◽  
Detection Algorithm ◽  
Model Parameters ◽  
Detection Accuracy ◽  
Detection Model ◽  
Embedded Platform

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%.

Far Infrared Imaging Sensor for Mass Production of Night Vision and Pedestrian Detection Systems

2012 ◽  
pp. 301-312 ◽  
Author(s):  
Emmanuel Bercier ◽  
Patrick Robert ◽  
David Pochic ◽  
Jean-Luc Tissot ◽  
Agnes Arnaud ◽  
...  
Keyword(s):  
Mass Production ◽  
Infrared Imaging ◽  
Pedestrian Detection ◽  
Far Infrared ◽  
Night Vision ◽  
Detection Systems ◽  
Imaging Sensor

Multi Stereo-Based Pedestrian Detection by Daylight and Far-Infrared Cameras

2009 ◽  
pp. 371-401 ◽  
Author(s):  
Massimo Bertozzi ◽  
Alberto Broggi ◽  
Mirko Felisa ◽  
Stefano Ghidoni ◽  
Paolo Grisleri ◽  
...  
Keyword(s):  
Pedestrian Detection ◽  
Far Infrared ◽  
Infrared Cameras
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