HOG-LBP Adaptable Fused Features Based Method for Forbidden Traffic Signs Detection

This paper presents a novel approach of detecting forbidden traffic signs.In this approach,a HOG-LBP(histograms of oriented gradients-local binary patterns) adaptable fused feature is proposed.The traffic sign image is cut into several non-overlapping blocks,in each block,the HOG and LBP features are weighted serial fused based on each block's gradient value.Then to get classifier which is used in detecting forbidden traffic signs,SVM(support vector machine) is used in training features.The experimental results show that proposed fusion feature is more effective and feasible than HOG,LBP and simple serial fused HOG-LBP feature.