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http://dx.doi.org/10.7840/kics.2012.37A.8.639

Fast On-Road Vehicle Detection Using Reduced Multivariate Polynomial Classifier  

Kim, Joong-Rock (연세대학교 전기전자공학과 영상인식 연구실)
Yu, Sun-Jin (연세대학교 전기전자공학과 영상인식 연구실)
Toh, Kar-Ann (연세대학교 전기전자공학과 영상인식 연구실)
Kim, Do-Hoon (전자부품 연구원 무선플랫폼센터)
Lee, Sang-Youn (연세대학교 전기전자공학과 영상인식 연구실)
Publication Information
The Journal of Korean Institute of Communications and Information Sciences / v.37, no.8A, 2012 , pp. 639-647 More about this Journal
Abstract
Vision-based on-road vehicle detection is one of the key techniques in automotive driver assistance systems. However, due to the huge within-class variability in vehicle appearance and environmental changes, it remains a challenging task to develop an accurate and reliable detection system. In general, a vehicle detection system consists of two steps. The candidate locations of vehicles are found in the Hypothesis Generation (HG) step, and the detected locations in the HG step are verified in the Hypothesis Verification (HV) step. Since the final decision is made in the HV step, the HV step is crucial for accurate detection. In this paper, we propose using a reduced multivariate polynomial pattern classifier (RM) for the HV step. Our experimental results show that the RM classifier outperforms the well-known Support Vector Machine (SVM) classifier, particularly in terms of the fast decision speed, which is suitable for real-time implementation.
Keywords
pattern recognition; object detection; vehicle detection; computer vision; robot vision;
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