DocumentCode :
3482019
Title :
Mono-camera based side vehicle detection for blind spot detection systems
Author :
Jang Woon Baek ; Eunryung Lee ; Mi-Ryung Park ; Dae-Wha Seo
Author_Institution :
Automotive IT Platform Res. Sect., ETRI, Daegu, South Korea
fYear :
2015
fDate :
7-10 July 2015
Firstpage :
147
Lastpage :
149
Abstract :
This paper proposes a vision-based side vehicle detection for blind spot detection systems. The proposed algorithm uses a HoG cascade classifier in order to detect vehicles, and tracks the detected vehicles with Kalman filter. The proposed algorithm performs a periodical vehicle detection instead of every frame vehicle detection. And the proposed algorithm reduces the detecting image size by downscaling the original image and setting the region of interest where vehicles can exist. As a result, we can reduce the processing time for vehicle detection. Also, the proposed algorithm uses a false alarm reducing methods by control the reliability points at vehicle tracking. We evaluated the performance of the proposed algorithm in terms of processing time and detection ratio. At target board, the proposed algorithm has 40 frames per second, which meets the real time requirements of the ADAS systems. The detection ratio of the proposed algorithm is over 96 % at both original image size and downscale image size.
Keywords :
Kalman filters; driver information systems; image classification; object detection; road vehicles; ADAS system; HoG cascade classifier; Kalman filter; blind spot detection systems; false alarm; monocamera; region-of-interest; reliability point; vehicle tracking; vision-based side vehicle detection; Accidents; Boards; Cameras; Classification algorithms; Mirrors; Vehicle detection; Vehicles; blind spot detection; classifier; mono camera; side-vehicle detection;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Ubiquitous and Future Networks (ICUFN), 2015 Seventh International Conference on
Conference_Location :
Sapporo
ISSN :
2288-0712
Type :
conf
DOI :
10.1109/ICUFN.2015.7182522
Filename :
7182522
Link To Document :
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