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Title: Moving vehicle detection for automatic traffic monitoring
Authors: Zhou, J
Gao, D
Zhang, DD 
Keywords: Principal component analysis (PCA)
Statistical learning
Support vector machine (SVM)
Video-based traffic monitoring
Issue Date: Jan-2007
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on vehicular technology, Jan. 2007, v. 56, no. 1, p. 51-59 How to cite?
Journal: IEEE transactions on vehicular technology 
Abstract: A video-based traffic monitoring system must be capable of working in various weather and illumination conditions. In this paper, we will propose an example-based algorithm for moving vehicle detection. Different from previous works, this algorithm learns from examples and does not rely on any a priori model for vehicles. First, a novel scheme for adaptive background estimation is introduced. Then, the image is divided into many small nonoverlapped blocks. The candidates of the vehicle part can be found from the blocks if there is some change in gray level between the current image and the background. A low-dimensional feature is produced by applying principal component analysis to two histograms of each candidate, and a classifier based on a support vector machine is designed to classify it as a part of a real vehicle or not. Finally, all classified results are combined, and a parallelogram is built to represent the shape of each vehicle. Experimental results show that our algorithm has a satisfying performance under varied conditions, which can robustly and effectively eliminate the influence of casting shadows, headlights, or bad illumination.
ISSN: 0018-9545
EISSN: 1939-9359
DOI: 10.1109/TVT.2006.883735
Rights: © 2007 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
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