An HOG-LGBPHS human detector with dimensionality reduction by PLS

Abstract

By combining Histogram of Oriented Gradient (HOG), which is based on evaluating well-normalized local histograms of image gradient orientations in a dense grid, with Local Gabor Binary Pattern Histogram Sequence (LGBPHS), which concatenate the histograms of all the local regions of all the local Gabor magnitude binary pattern maps, as a feature set, we proposed a novel human detection feature. We employ Partial Least Squares (PLS) analysis, an efficient dimensionality reduction technique, to project the feature onto a much lower dimensional subspace (9 dimensions, reduced from the original over 12000). We test the new feature in INRIA person dataset by using a linear SVM, and it yields an error rate of 1.35% with a false negatives (FN) rate of 0.46% and a false positive (FP) rate of 0.89%, while the error rate of HOG is 7.11% with a FN rate of 4.09% and a FP rate of 3.02%, and the error rate of LGBPHS is 13.55% with a FN rate of 4.94% and a FP rate of 8.61%.