Pedestrian Detection and Tracking in Far Infrared Images

2005 ◽  
Author(s):  
M. Yasuno ◽  
N. Yasuda ◽  
M. Aoki
Keyword(s):  
Pedestrian Detection ◽  
Far Infrared ◽  
Infrared Images ◽  
Detection And Tracking

scholarly journals Pedestrian detection in far infrared images

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

scholarly journals Pedestrian Detection in Far Infrared Images based on the use of Probabilistic Templates

2007 ◽  
Author(s):  
M. Bertozzi ◽  
A. Broggi ◽  
C. Hilario Gomez ◽  
R. I. Fedriga ◽  
G. Vezzoni ◽  
...  
Keyword(s):  
Pedestrian Detection ◽  
Far Infrared ◽  
Infrared Images

scholarly journals Saliency-based Pedestrian Detection in Far Infrared Images

IEEE Access ◽  
2017 ◽  
pp. 1-1 ◽  
Author(s):  
Yingfeng Cai ◽  
Ze Liu ◽  
Hai Wang ◽  
Xiaoqiang Sun
Keyword(s):  
Pedestrian Detection ◽  
Far Infrared ◽  
Infrared Images

Far infrared pedestrian detection and tracking for night driving

Robotica ◽  
2010 ◽  
Vol 29 (4) ◽  
pp. 495-505 ◽  
Author(s):  
Daniel Olmeda ◽  
Arturo de la Escalera ◽  
José María Armingol
Keyword(s):  
Human Body ◽  
Probabilistic Models ◽  
Unscented Kalman Filter ◽  
Pedestrian Detection ◽  
Infrared Camera ◽  
Far Infrared ◽  
Moving Vehicle ◽  
Average Temperature ◽  
Detection And Tracking ◽  
Subsequent Step

SUMMARYThis paper presents a module for pedestrian detection from a moving vehicle in low-light conditions. The algorithm make use of a single far infrared camera based on a microbolometer. Images of the area ahead of the vehicle are analyzed to determine if any pedestrian might be in its trajectory. Detection is achieved by searching for distributions of temperatures in the scene similar to that of the human body. Those areas with an appropriate temperature, size, and position in the image are classified, by means of a correlation between them and some probabilistic models, which represents the average temperature of the different parts of the human body. Finally, those pedestrians found are tracked in a subsequent step, using an unscented Kalman filter. This final stage of the algorithm enables the algorithm to predict the trajectory of the pedestrian, in a way that does not depend on the movement of the camera. The aim of this system is to warn the vehicle's driver and reduce the reaction time in case an emergency break is necessary.

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