Title

Laser Intensity Vehicle Classification System Based On Random Neural Network

Keywords

Image processing; Loop detectors; Range sensor; Vehicle classification

Abstract

This paper presents a Laser Intensity Vehicle Classification System (LIVCS) based upon imagery obtained from range sensors (called LIVCS). Current systems that utilize loop detectors, video cameras, and range sensors have deficiencies. The loop detectors have high failure rates due to pavement failures and poor maintenance. Video based systems and range sensors do not perform well in deteriorated atmospheric conditions (such as rain and fog). The developed generations of image based range sensors offer the promise of sensors that are less sensitive to deteriorated environmental conditions. LIVCS system extracts features of laser intensity images, produced by laser sensory units. These features are used to train a random neural network (RNN). The LIVCS system recalls its trained RNN for classification of vehicles. This technique outperforms loop detectors, video cameras, and range data techniques in deteriorated environmental conditions. Copyright 2005 ACM.

Publication Date

12-1-2005

Publication Title

Proceedings of the Annual Southeast Conference

Volume

1

Number of Pages

131-135

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1145/1167350.1167372

Socpus ID

77953781779 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/77953781779

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