Pedestrian recognition based on hierarchical codebook of SURF features in visible and infrared images

One of the main challenges in Intelligent Vehicle is recognition of road obstacles. Our goal is to design a real-time, precise and robust pedestrian recognition system. We choose to use Speeded Up Robust Features (SURF) and a Support Vector Machine (SVM) classifier in order to perform the recognition task. Our main contribution is a method for fast computation of discriminative features for pedestrian recognition. Fast features extraction is assured by using a hierarchical codebook of scale and rotation-invariant SURF features. We evaluate our approach for pedestrian recognition in a set of images where people occur at different scales and in difficult recognition situations. The system shows good performance in visible and especially in infrared images. Besides, experimental results show that the hierarchical structure presents a major interest not only for maintaining a reasonable feature extraction time, but also for improving classification results.