Cascade of Complementary Features for Fast and Accurate Pedestrian Detection

We propose a cascade of two complementary features classifiers to detect pedestrians from static images quickly and accurately. Co-occurrence Histograms of Oriented Gradients (CoHOG) descriptors have a strong classification capability but are extremely high dimensional. On the other hand, Haar-like features are computationally efficient but not highly discriminative for extremely varying texture and shape information such as pedestrians with different clothing and stances. Therefore, the combination of both features enables fast and accurate pedestrian detection. Our framework comprises a cascade of Haar based weak classifiers followed by a CoHOG-SVM classifier. Additionally, we propose reducing CoHOG descriptor dimensionality using Independent Component Analysis (ICA). The experimental results on the Daimler Chrysler and INRIA benchmark datasets show that we can reach very close accuracy to the most accurate CoHOG-only classifier but in less than 1/1000 of its computational cost.