Wavelet-based vehicle tracking for automatic traffic surveillance

Abstract

A system for wavelet-based vehicle tracking for automatic traffic surveillance is proposed. In order to meet real-time requirements, we use adaptive thresholding and a wavelet-based neural network (NN), which achieves low computational complexity, accuracy of localization, and noise robustness has been considered for vehicle tracking. The proposed system consists of three steps: moving region extraction, vehicle recognition and vehicle tracking. First, moving regions are extracted by performing a frame difference analysis on two consecutive frames using adaptive thresholding. Second, the wavelet-based NN is used for recognizing the vehicles in the extracted moving regions. The wavelet transform is adopted to decompose an image and a particular frequency band is selected for input of the NN for vehicle recognition. Third, vehicles are tracked by using position coordinates and wavelet features difference values for correspondence in recognized vehicle regions. Experimental results of the proposed system can be useful for a traffic surveillance system.