Human detection using HOG-HSC feature and PLS

By combining histogram of oriented gradient and histograms of shearlet coefficients,which analyzes images at multiple scales and orientations based on shearlet transforms,as the feature set,we proposed a novel human detection feature.We employ partial least squares analysis,an efficient dimensionality reduction technique,to project the feature onto a much lower dimensional subspace.We test it in INRIA person dataset by using a linear SVM,and it yields an error rate of 1.38% with a false negatives(FN) rate of 0.40% and a false positive(FP) rate of 0.98%,while the error rate of HOG is 7.11%,with a FN rate of 4.09% and a FP rate of 3.02%.