Boosted parametric model for human detection
Tongzhi Li, Xiaoqing Ding, Shengjin Wang · 2008
In this paper we discuss the issue of classifiers combined with Histogram of Oriented Gradients (HOG) descriptors for human detection. And we present a method that combines AdaBoost learning with HOG descriptors. The weak learners used in our algorithm are based on weighted Modified Quadratic Discriminant Functions (MQDF) which is a parametric model. We evaluate our algorithm on the INRIA person dataset. And the experimental results show that our approach achieves a comparable performance with the state of art methods both on accuracy and speed.