Nowadays, there are many different types of mobility aids for elderly people. Nevertheless, these devices may lead to accidents, depending on the terrain where they are being used. In this context, the goal of the EyeWalker project is to develop a ultralight computer vision device for users with mobility problems. One of the main objective of this work is to develop a ground change detection module that will warn the user before entering dangerous terrains or hostile situations. This software component integrated on a ”smart” walker will be able to react in real time, to operate both indoor and outdoor, as well as in familiar or unfamiliar environments. Specifically, we propose a classification algorithm using colour and texture as a descriptor to detect ground changes. In our classifier, the distributions of HSV colours and Local Edge Patterns are used to compare the similarity between the current frame and the average of the k previous frames. To compare similarities, we used four different techniques (Histogram Intersection, Kolmogorov-Smirnov, Cumulative Integral and Artificial Neural Networks) with outdoor training images. Preliminary results reveal that artificial neural networks achieved the best performances.