LPP-HOG: A New Local Image Descriptor for Fast Human Detection

Abstract

LPP (locality preserving projection), as a linear version of manifold learning algorithm, has attracted considerable interests in recent years. For real time applications, the response time is required to be as short as possible. In this paper, a new local image descriptor-LPP-HOG (histograms of oriented gradients) for fast human detection is presented. We employ HOG features extracted from all locations of a grid on the image as candidates of the feature vectors. LPP is applied to these HOG feature vectors to obtain the low dimensional LPP-HOG vectors. The selected LPP-HOG feature vectors are used as an input of linear SVM to classify the given input into pedestrian/non-pedestrian. We also present results showing that using these descriptors in human detection application results in increased accuracy and faster matching.