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Vehicle Detection Scheme Based on a Boosting Classifier with Histogram of Oriented Gradient (HOG) Features and Image Segmentation]  

Choi, Mi-Soon (안동대학교 컴퓨터공학과)
Lee, Jeong-Hwan (안동대학교 전자공학과)
Roh, Tae-Moon (한국전자통신연구원)
Shim, Jae-Chang (안동대학교 컴퓨터공학과)
Publication Information
Journal of KIISE:Computing Practices and Letters / v.16, no.10, 2010 , pp. 955-961 More about this Journal
Abstract
In this paper, we describe a study of a vehicle detection method based on a Boosting Classifier which uses Histogram of Oriented Gradient (HOG) features and Image Segmentation techniques. An input image is segmented by means of a split and merge algorithm. Then, the two largest segmented regions are removed in order to reduce the search region and speed up processing time. The HOG features are then calculated for each pixel in the search region. In order to detect the vehicle region we used the AdaBoost (adaptive boost) method, which is well known for classifying samples with two classes. To evaluate the performance of the proposed method, 537 training images were used to train and learn the classifier, followed by 500 non-training images to provide the recognition rate. From these experiments we were able to detect the proper image 98.34% of the time for the 500 non-training images. In conclusion, the proposed method can be used for detecting the location of a vehicle in an intelligent vehicle control system.
Keywords
vehicle detection; split and merge; boosting_based classifier; HOG features;
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