Boosting a heterogeneous pool of fast HOG features for pedestrian and sign detection

This paper presents a fast Histogram of Oriented Gradients (HOG) based weak classifier that is extremely fast to compute and highly discriminative. This feature set has been developed in an effort to balance the required processing and memory bandwidth so as to eliminate bottlenecks during run time evaluation. The feature set is the next generation in a series of features based on a novel precomputed image for HOG based features. It contains features which are more balanced in terms of processing and memory requirements than its predecessors, has a larger and richer feature space, and is more discriminant on a per feature basis. In terms of computational complexity it is a heterogeneous feature set. I.e. it has fast and slow variants. In order to optimize our feature selections between the faster and slower features available we implement a recently proposed modification to the RealBoost feature selection rule. This modification provides an additional means to balance processing and memory bandwidth on ordinary PC architectures. This feature set is suitable for use within typical boosting frameworks. It is compared to Haar and Rectangular HOG features, as well the related feature HistFeat. The new feature set contains two variants, LiteHOG and LiteHOG+, which we compare. Both LiteHOG and LiteHOG+ show promising results on road sign and pedestrian detection tasks.