Key Learning Features as Means for Terrain Classification

Abstract

Modern vehicles seek autonomous subsystems adaptability to ever-changing terrain types in pursuit of enhanced drivability and maneuverability. The impact of key features on the classification accuracy of terrain types using a colour camera is investigated. A handpicked combination of texture and colour as well as a simple unsupervised feature representation is proposed. Although the results are restricted to only four classes {grass, tarmac, dirt, gravel} the learned features can be tailored to suit more classes as well as different scenarios altogether. The novel aspect stems from the feature representation itself as a global gist for three quantities of interest within each image: background, foreground and noise. In addition to that, the frequency affinity of the Gabor wavelet gist component to perspective images is mitigated by inverse homography mapping. The emphasis is thus on feature selection in an unsupervised manner and a framework for integrating learned features with standard off the shelf machine learning algorithms is provided. Starting with a colour hue and saturation histogram as fundamental building block, more complex features such as GLCM, k-means and GMM quantities are gradually added to observe their integrated effect on class prediction for three parallel regions of interest. The terrain classification problem is tackled with promising results using a forward facing camera.