Vehicle detection and classification in shadowy traffic images using wavelets and neural networks

Tien-Hsin Chao; Brian Lau; Youngchul Park

doi:10.1117/12.267139

17 February 1997 Vehicle detection and classification in shadowy traffic images using wavelets and neural networks

Tien-Hsin Chao, Brian Lau, Youngchul Park

Proceedings Volume 2902, Transportation Sensors and Controls: Collision Avoidance, Traffic Management, and ITS; (1997) https://doi.org/10.1117/12.267139
Event: Photonics East '96, 1996, Boston, MA, United States

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.

Citation Download Citation

Tien-Hsin Chao, Brian Lau, and Youngchul Park "Vehicle detection and classification in shadowy traffic images using wavelets and neural networks", Proc. SPIE 2902, Transportation Sensors and Controls: Collision Avoidance, Traffic Management, and ITS, (17 February 1997); https://doi.org/10.1117/12.267139

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