PT Unknown
AU Yainuvis Socarras
   David Vazquez
   Antonio Lopez
   David Geronimo
   Theo Gevers
TI Improving HOG with Image Segmentation: Application to Human Detection
BT 11th International Conference on  Advanced Concepts for Intelligent Vision Systems
PY 2012
BP 178
EP 189
VL 7517
DI 10.1007/978-3-642-33140-4_16
LA English
DE Segmentation; Pedestrian Detection
AB 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.
ER