%0 Conference Proceedings
%T Improving HOG with Image Segmentation: Application to Human Detection
%A Yainuvis Socarras
%A David Vazquez
%A Antonio Lopez
%A David Geronimo
%A Theo Gevers
%E J. Blanc-Talon et al.
%B 11th International Conference on  Advanced Concepts for Intelligent Vision Systems
%D 2012
%V 7517
%I Springer Berlin Heidelberg
%@ 0302-9743
%@ 978-3-642-33139-8
%G English
%F Yainuvis Socarras2012
%O ADAS;ISE
%O exported from refbase (http://refbase.cvc.uab.es/show.php?record=1980), last updated on Tue, 18 Oct 2016 13:10:11 +0200
%X In this paper we improve the histogram of oriented gradients (HOG), a core descriptor of state-of-the-art object detection, by the use of higher-level information coming from image segmentation. The idea is to re-weight the descriptor while computing it without increasing its size. The benefits of the proposal are two-fold: (i) to improve the performance of the detector by enriching the descriptor information and (ii) take advantage of the information of image segmentation, which in fact is likely to be used in other stages of the detection system such as candidate generation or refinement.We test our technique in the INRIA person dataset, which was originally developed to test HOG, embedding it in a human detection system. The well-known segmentation method, mean-shift (from smaller to larger super-pixels), and different methods to re-weight the original descriptor (constant, region-luminance, color or texture-dependent) has been evaluated. We achieve performance improvements of 4:47% in detection rate through the use of differences of color between contour pixel neighborhoods as re-weighting function.
%K Segmentation
%K Pedestrian Detection
%U http://refbase.cvc.uab.es/files/SLV2012a.pdf
%U http://dx.doi.org/10.1007/978-3-642-33140-4_16
%P 178-189