Evidential combination of SVM road obstacle classifiers in visible and far infrared images
Bassem Besbes, Sonda Ammar, Yousri Kessentini, Alexandrina Rogozan, Abdelaziz Bensrhair · 2011
In this work, we focus on an improvement of a road obstacle recognition system using SVM based classifiers combination. The improvement relies on the use of Dempster-Shafer theory (DST) to combine in a finer way the outputs of SVM classifiers. The SVM classifiers were trained on different local and global features based on Speeded Up Robust Features (SURF) extracted from both visible and far-infrared images. A two-stage recognition method is also proposed to reduce the complexity of the overall system. The experiments are conducted on a set of images where obstacles occur at different scales, shapes and in difficult recognition situations. They show significant improvements while using DST combination compared to the classical combination strategies.