Active Pedestrian Safety Systems

Abstract

We study the question of feature sets for robust visual object recognition; adopting linear Support Vector Machine (SVM) based
human detection as a test case. After reviewing existing edge and gradient based descriptors, we show experimentally that grids
of Histograms of Oriented Gradient (HOG) descriptors significantly outperform existing feature sets for human detection. We
study the influence of each stage of the computation on performance, concluding that fine-scale gradients, fine orientation
binning, relatively coarse spatial binning, and high-quality local contrast normalization in overlapping descriptor blocks are all
important for good results. The new approach gives near-perfect separation on the original. Massachusetts Institute of
Technology (MIT) pedestrian database, so we introduce a more challenging dataset containing over 1800 annotated human
images with a large range of pose variations and backgrounds.