Paper Title
Literature review on “cascade object detection using deformable part model”

This paper describes a general method for building cascade classifiers from part-based deformable models such as pictorial structures. This paper focuses primarily on the case of star-structured models and show how a simple algorithm based on partial hypothesis pruning can speed up object detection by more than one order of magnitude without sacrificing detection accuracy. It based on two algorithms; the cascade variant dynamic programming algorithm fills values in DP tables and training algorithm for the thresholds used in the cascade