A new type of hybrid features for human detection
Azadeh Sadat Mozafari, Mansour Jamzad · 2012
Human detection is one of the hard problems in object detection field. There are many challenges like variation in human pose, different clothes, non-uniform illumination, cluttered background and occlusion which make this problem much harder than other object detection problems. Defining good features, which can be robust to this wide range of variations, is still an open issue in this field. To overcome this challenge, in this paper we proposed a new set of hybrid features. We combined the Histogram of Oriented Gradient (HOG) with the new features called Histogram of Small Edges (HOSE) which is introduced in this paper. These two kinds of features have two different approaches for extracting features and have the complementary role for each other. Our experimental results on INRIA dataset showed that using the proposed hybrid features provides better detection rate in comparison with state of the art features.