Efficient pedestrian detection with group lasso

In this paper we deal with pedestrian detection and propose the use of group lasso to learn from data a compact and meaningful representation out of a high dimensional dictionary of local features. Group lasso, a regularized method with a sparsity-enforcing penalty term, has the very nice property of performing feature selection while preserving the internal structure of the dictionary. In our study we consider in particular variable-size HoGs, whose internal structure is composed by cells and blocks: since the entries of a block need to be computed together, the feature selection process is designed so to keep them or discard them all. The detection algorithm we obtain is a very neat procedure, simple to train and computationally efficient, which allows us to achieve a very good compromise between performance and computational cost, making the method very appropriate for video surveillance applications.