Monocular multi-human detection using Augmented Histograms of Oriented Gradients

Abstract

We introduce an Augmented Histograms of Oriented Gradients (AHOG) feature for human detection from a nonstatic camera. We increase the discriminating power of original Histograms of Oriented Gradients (HOG) feature by adding human shape properties, such as contour distances, symmetry, and gradient density. Based on the biological structure of human shape, we impose the symmetry property on HOG features by computing the similarity between itself and its’ symmetric pair to weight HOG features. After that, the capability of describing human features is much better than the original one, especially when the humans are moving across. We also augment the gradient density into features to mitigate the influences caused by repetitive backgrounds. In the experiments, our method demonstrates most reliable performance at any view of targets.