Fast On-Road Vehicle Detection Using Reduced Multivariate Polynomial Classifier 


Vol. 37,  No. 8, pp. 639-647, Aug.  2012


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  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.

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  Cite this article

[IEEE Style]

J. Kim, S. Yu, K. Toh, D. Kim, S. Lee, "Fast On-Road Vehicle Detection Using Reduced Multivariate Polynomial Classifier," The Journal of Korean Institute of Communications and Information Sciences, vol. 37, no. 8, pp. 639-647, 2012. DOI: .

[ACM Style]

Joongrock Kim, Sunjin Yu, Kar-Ann Toh, Do-hoon Kim, and Sangyoun Lee. 2012. Fast On-Road Vehicle Detection Using Reduced Multivariate Polynomial Classifier. The Journal of Korean Institute of Communications and Information Sciences, 37, 8, (2012), 639-647. DOI: .

[KICS Style]

Joongrock Kim, Sunjin Yu, Kar-Ann Toh, Do-hoon Kim, Sangyoun Lee, "Fast On-Road Vehicle Detection Using Reduced Multivariate Polynomial Classifier," The Journal of Korean Institute of Communications and Information Sciences, vol. 37, no. 8, pp. 639-647, 8. 2012.

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pISSN : 1226 - 4717
eISSN : 2287 - 3880