Vehicle detection algorithm using hypothesis generation and verification

Authors:
Quoc Bao Truong;Byung Ryong Lee
Affiliations:
Department of Mechanical and Automotive Engineering, University of Ulsan, Ulsan, Korea;School of Mechanical and Automotive Engineering, University of Ulsan, Ulsan, Korea
Venue:
ICIC'09 Proceedings of the 5th international conference on Emerging intelligent computing technology and applications
Year:
2009

Citing 2
Cited 0

A Trainable System for Object Detection

International Journal of Computer Vision - special issue on learning and vision at the center for biological and computational learning, Massachusetts Institute of Technology
Eigenfaces for recognition

Journal of Cognitive Neuroscience

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Abstract

In this paper, we present a two-stage vision-based approach to detect front and rear vehicle views in road scene images using eigenspace and a support vector machine for classification. The first stage is hypothesis generation (HG), in which potential vehicles are hypothesized. During the hypothesis generation step, we use a vertical, horizontal edge map to create potential regions where vehicles may be present. In the second stage verification (HV) step, all hypotheses are verified by using a Principle Component Analysis (PCA) for feature extraction and a Support Vector Machine (SVM) for classification, which is robust for both front and rear vehicle view detection problems. Our methods have been tested on different real road images and show very good performance.