In this paper we perform an applied comparative study of popular HOG based human detection and a state-of-the-art
pose adaptive method that uses shape-based model construction. Both methods are implemented with kernel SVM,
instead of linear SVM. Detailed performance evaluation is carried out on MIT pedestrian dataset and INRIA person
dataset. This study shows that, although pose adaptive method has no significant advantage compared to the HOG based
approach on those datasets, the pose adaptive approach is more efficient in detection and it has the capability to segment
the human shape from images while carrying out detection which can be advantageous in many applications.