Vision-Based Vehicle Detection in Real Traffic Environment Using Fast Wavelet Transform and Kalman Filter

Abstract:

A new vision-based approach for robust vehicle detection is addressed in this study. First, fast wavelet transform (FWT) is proposed to extract image texture, while grey level co-occurrence matrix (GLCM) is employed to measure and analyze the extracted texture. Then, vehicles can be extracted because the vehicle sections and the shadow sections have different textures in the foreground image. Moreover, we put forward the state and observation matrixes of Kalman filter which can be used to track vehicles under complicated traffic scenes. Experimental results in real traffic scenes show that the proposed methods are effective and efficient.

• Periodical: Advanced Materials Research (Volumes 998-999)
• Pages: 717-722
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.998-999.717
• Online since: July 2014
• Authors: Fu Qing Zhuo*, Pei Qun Lin, Yu Mu Gu
• Keywords: Fast Wavelet Transform, Intelligent Transportation System (ITS), Kalman Filter, Vehicle Detection, Vehicle Tracking

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