Pixel Structure Based on Hausdorff Distance for Human Detection in Outdoor Environments

This paper proposes a novel method for human detection from static images based on pixel structure of input images. In training stage, all sample images consisting of human images and non-human images are used to construct a Hausdorff distance map based on statistically analyzing the difference between the different blocks on each original image. A projection matrix will be created with Linear Discriminant Method (LDM) based on the Hausdorff distance map. This projection matrix will be used to transform multidimensional feature vectors (distance maps of testing images) into a feature in a one-dimensional domain. The decision will be made on the simple one- dimensional feature domain according to a precalculated threshold to distinguish human figures from non-human figures. In comparison with the common method based on Mahalanobis distance maps, the proposed method based on Hausdorff distance maps performs much better. Encouraging experimental results have been obtained using 800 human images and 800 non-human images. .