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Proceedings Paper

Vehicle detection and classification in shadowy traffic images using wavelets and neural networks
Author(s): Tien-Hsin Chao; Brian Lau; Youngchul Park
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Paper Abstract

A vision-based traffic surveillance processor is being developed at JPL. This processor uses innovative image segmentation and classification techniques for vehicles in freeway images, including those with large shadows. These results enable the computation of many useful traffic parameters. A wavelet-based algorithm has been developed for vehicle detection and segmentation. Specifically, two types of mother wavelet has been created and tested: the first for shape-size discrimination of vehicles from their background; and the second for locating where vehicles join their shadows, thus enabling segmentation of the vehicles from their shadows. Combining these two wavelets enables robust segmentation of vehicles from busy freeways. This method reduces the false-alarm rate in vehicle counts, since shadows are no longer mistaken for vehicles. We use neural networks for vehicle classification. To reduce system complexity and training time, we use, as preprocessors, several feature extraction methods, such as invariant-moment and Hermite-moment computations. This preprocessing enables orders of magnitude reductions in training time and a great increase in classification accuracy.

Paper Details

Date Published: 17 February 1997
PDF: 12 pages
Proc. SPIE 2902, Transportation Sensors and Controls: Collision Avoidance, Traffic Management, and ITS, (17 February 1997); doi: 10.1117/12.267139
Show Author Affiliations
Tien-Hsin Chao, Jet Propulsion Lab. (United States)
Brian Lau, Jet Propulsion Lab. (United States)
Youngchul Park, Jet Propulsion Lab. (United States)

Published in SPIE Proceedings Vol. 2902:
Transportation Sensors and Controls: Collision Avoidance, Traffic Management, and ITS
Alan C. Chachich; Marten J. de Vries, Editor(s)

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