SVM-based fast pedestrian recognition using a hierarchical codebook of local features

The performance of an object recognition system depends on both object representation and classification algorithms. On the one hand, Object representation by using local descriptors have become a very powerful representation of images. On the other hand, SVM has shown impressive learning and recognition performances. In this paper, we present a method for fast pedestrian classification by combining a SVM with a hierarchical codebook of local features augmented with reliable global features. When compared to SVM based on local matching kernels, our method provides significant improvement of recognition performances with a speed up in learning and classification time. We evaluate our approach on a set of far-infrared images where pedestrians occur at different scales and in difficult recognition situations. The experiment shows that our method performs a fast and reliable pedestrian recognition system.