Pedestrian Detection Using Boosted HOG Features
Zhenrui Wang, Yulan Jia, Hua Huang, Shuming Tang · 2008
This paper presents a novel approach in pedestrian detection in static images. The state-of-art feature named Histograms of Oriented Gradients (HOG) [1] is adopted as the basic feature which we modify and create a new feature using boosting algorithm. The detection is achieved by training a linear SVM with the boosted HOG feature. We experimentally demonstrate that our solution achieve comparable performance as the HOG algorithm on the INRIA pedestrian dataset yet considerably reduce storage requirement and simplify the computation in terms of elementary operations.