Fast Vehicle Detection Using a Disparity Projection Method

Abstract

The stereo vision could obtain the 3-D coordinate of the detected object by computing the disparity of the corresponding image points. However, on account of the time complexity and the low robustness of the image matching algorithm, it is seldom used in large-scale scene. This paper puts forward a new vehicle detection method, which simplifies the massive Fourier transformation in the image matching process. The method converts the 2-D Fourier transformation to 1-D with the dimensionality reduction of reused Fourier transformation. Meanwhile, 1-D Fourier transformation of the fast image matching model is also derived. The coarse-to-fine pyramid search strategy is used according to the gradient information of each depth map adaptively. The adjacent area of the same depth is obtained with a larger matching weight which improves the matching accuracy and robustness. The model can be used in the fitting and projection transformation of road plane after background extraction. Thus reduce the complexity of vehicle segmentation and enhance the robustness of vehicle detection. The experimental results show the method can effectively achieve the large-scale and real-time detection. It is adaptive to various illumination changes and impervious to shadow and occlusion.