Combining SURF-based local and global features for road obstacle recognition in far infrared images

This paper describes a road obstacle classification system that recognizes both vehicles and pedestrians in far-infrared images. Different local and global features based on Speeded Up Robust Features (SURF) were investigated and then selected in order to extract a discriminative signature from the infrared spectrum. First, local features representing the local appearance of an obstacle, are extracted from a codebook of scale and rotation-invariant SURF features. Second, global features were used since they provide complementary information by characterizing shape and texture. When compared with the state-of-the-art Haar and Gabor wavelet features, our method provides significant improvement of recognition performances. Moreover, since our SURF based representation is invariant to the scale and the number of local features extracted from objects, our system performs the recognition task without resizing images. Our system was evaluated on a set of far-infrared images where obstacles occur at different scales and in difficult recognition situations. By using a multi-class SVM approach, accuracy rates of 91.51% has been achieved on Surf-based representation, while a maximum rate of 89.11% was achieved on wavelet-based representation.