Road detection in Arid Environments Using Uniformly Distributed Random Based Features

摘要

The capability of detecting an unpaved road in arid environments can greatly enhance an explosive hazard detection system. One approach is to segment out the off-road area and the area above the horizon, which is considered to be irrelevant for the task in hand. Segmenting out irrelevant areas, such as the region above the horizon, allows the explosive hazard detection system to process a smaller region in a scene, enabling a more computationally complex approach. In this paper, we propose a novel approach for speeding up the detection algorithms based on random projection and random selection. Both methods have a low computational cost and reduce the dimensionality of the data while approximately preserving, with a certain probability, the pair-wise point distances. Dimensionality reduction allows any classifier employed in our proposed algorithm to consume fewer computational resources. Furthermore, by applying the random projections directly to image intensity patches, there is no feature extraction needed. The data used in our proposed algorithms are obtained from sensors on board a U.S. Army countermine vehicle. We tested our proposed algorithms on data obtained from several runs on an arid climate road. In our experiments we compare our algorithms based on random projection and random selection to Principal Component Analysis (PCA), a popular dimensionality reduction method.