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Stereo vision-based improving cascade classifier learning for vehicle detection

Title
Stereo vision-based improving cascade classifier learning for vehicle detection
Authors
Kim, JonghwanLee, Chung-HeeLim, Young-ChulKwon, Soon
DGIST Authors
Kim, Jonghwan; Lee, Chung-Hee; Lim, Young-Chul; Kwon, Soon
Issue Date
2011
Citation
7th International Symposium on Visual Computing, ISVC 2011, 6939 LNCS(PART 2), 387-397
Type
Conference
Article Type
Conference Paper
ISSN
0302-9743
Abstract
In this article, we describe an improved method of vehicle detection. AdaBoost, a classifier trained by adaptive boosting and originally developed for face detection, has become popular among computer vision researchers for vehicle detection. Although it is the choice of many researchers in the intelligent vehicle field, it tends to yield many false-positive results because of the poor discernment of its simple features. It is also excessively slow to processing speed as the classifier's detection window usually searches the entire input image. We propose a solution that overcomes both these disadvantages. The stereo vision technique allows us to produce a depth map, providing information on the distances of objects. With that information, we can define a region of interest (RoI) and restrict the vehicle search to that region only. This method simultaneously blocks false-positive results and reduces the computing time for detection. Our experiments prove the superiority of the proposed method. © 2011 Springer-Verlag.
URI
http://hdl.handle.net/20.500.11750/3920
DOI
10.1007/978-3-642-24031-7_39
Publisher
Springer
Related Researcher
Files:
There are no files associated with this item.
Collection:
Convergence Research Center for Future Automotive Technology2. Conference Papers
Division of IoT∙Robotics Convergence Research2. Conference Papers


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