Gaussian Mixture Model based road signature classification for robot navigation

For any autonomous system it is very important to acquire the knowledge of the surrounding environment. Images and videos acquired by the vision based sensors can provide meaningful information about the environment, which can be very useful for the navigation of autonomous system like mobile robots. To extract road information from image frames for navigation purpose they have to be classified. Classification is the process of assigning label to the image pixels. Gaussian Mixture Model (GMM) is a model based segmentation method to group image pixels, where the parameters of the model are learned by Expectation Maximization (EM) algorithm. This paper we introduce a top-down supervised learning to assign logical labels to multiple modes created by GMM. This paper also explains the rejection criteria implemented in GMM based classification, which ensures that only pixels with strong road signature are assigned to road class. Contiguity is also applied to get robust classification output. These enable meaningful classification of images of same or similar scenes.