Human detection and tracking using new features combination in particle filter framework

Abstract

Human tracking is an interesting topic in computer vision domain. In this paper, a human detection and tracking algorithm based on new features combination in one camera system is proposed. In detection part, first, mixture of Gaussian background subtraction method is used to find moving regions, then histogram of oriented gradient (HOG) feature of these regions are extracted. At the end, SVM classifier is used to distinguish human from non-human according to their HOG features. In tracking part, first, color, cellular local binary pattern (Cell-LBP) and HOG features of humans are extracted, then their next positions are estimated using particle filter framework. Color, Cell-LBP and HOG features are used to model humans. Color is an effective feature in dealing with object deformation and partial occlusion but has some restriction in cases where background or objects have same color. Cell-LBP is an improved texture descriptor that is robust against partial occlusion, this feature compensates color's restriction. HOG is a shape descriptor that can separate humans from background and is robust against illumination changes. Combination of these three features improves tracking result despite challenges like partial occlusion, object's deformation and illumination changes. Experimental results show advantage of the proposed algorithm.