Vision-Based Vehicle Detection in Real Traffic Environment Using Fast Wavelet Transform and Kalman Filter
Year: 2014
Type: article
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.
Source: Advanced materials research
Authors: Fu Qing Zhuo, Pei Qun Lin, Yu Mu Gu
Institution: South China University of Technology
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W1978202791