Pedestrian detection via Wavelet Fractal Signature and Support Vector Machine

This paper presents a novel pedestrian detection approach. Firstly, Haar wavelet is employed to transform the input image into its sub-patterns with different resolution. And then, some relevant wavelet sub-patterns are selected to compute the fractal vector in different scales. Next, this fractal vector is assembled to be a Wavelet Fractal Signature vector, which is utilized in Support Vector Machine classifier. To validate this approach, some experiments based on Daimler´s Pedestrian Detection Benchmark are conducted; the experimental results show that the proposed approach has the advantages of compact feature expression form and high detection rate.