Traversable region detection based on fusion-features and partial least squares

Abstract

In order to detect the traversable region of automotive land vehicle (ALV), a multi-scale data analysis and representation method, shearlet transform is researched. Based on the feature that the descriptor calls Histograms of Shearlet Coefficients (HSC), a weighted HSC (WHSC) is proposed. Compared to HSC, WHSC uses the scale factor, which makes it better than HSC. We combine WHSC and color histogram in HSV color space as the fusion-feature, and use partial least squares (PLS) to project the high dimensional feature vectors onto a subspace. Also, support vector machine (SVM) is used based on linear kernel as the classification to solve traversable region detection. The experiment results suggest that for both in NUSTrobot dataset and OUTEX dataset, the method provided by this paper performs much better, and can detect the traversable regions in complex environments (e.g., different shadow and lighting conditions). Moreover, with the help of this method, the platform can achieve more functions.