Fast road detection and tracking in aerial videos

We propose a fast approach for detecting and tracking a specific road in aerial videos. It combines adaptive Gaussian Mixture Models (GMMs) to describe road colour distributions, and homography based tracking to track road geometries, where an efficient technique is developed to estimate homography transformations between two frames. Experiments are conducted on videos captured by our unmanned aerial vehicles. All the results demonstrate the effectiveness of our proposed method. We test 1755 frames from 5 videos. Our approach can achieve 0.032 seconds per frame and 2.64% segmentation error for images with 908 × 513 resolutions, on average.