Khaled F. Hussain
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.
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Index Terms
- Laser intensity vehicle classification system based on random neural network
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