Stereo vision-based improving cascade classifier learning for vehicle detection

  • Authors:

  • Jonghwan Kim;Chung-Hee Lee;Young-Chul Lim;Soon Kwon

  • Affiliations:

  • IT Convergence Research Department, Daegu Gyeongbuk Institute of Science & Technology, Republic of Korea;IT Convergence Research Department, Daegu Gyeongbuk Institute of Science & Technology, Republic of Korea;IT Convergence Research Department, Daegu Gyeongbuk Institute of Science & Technology, Republic of Korea;IT Convergence Research Department, Daegu Gyeongbuk Institute of Science & Technology, Republic of Korea

  • Venue:
  • ISVC'11 Proceedings of the 7th international conference on Advances in visual computing - Volume Part II
  • Year:
  • 2011

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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.