Wavelet based fuzzy clustering technique for the extraction of road objects

Detecting and recognizing road objects automatically is an important process in many applications such as traffic regulation and providing guidance for drivers and pedestrians. Fuzzy clustering using wavelets is proposed in this paper. Wavelets are used for pre-processing the image and the resulting image is then subjected to fuzzy c-means algorithm for clustering. After clustering, the image classification is done by an ensemble of multi-layer perceptron neural networks. This approach is used to classify road images into different road side objects like road, sky, and signs. A database using real-world roadside images from Transport and Main Roads (TMR) is used for evaluating the proposed approach. The results on the database using the proposed approach indicate that this approach using wavelets improves the recognition rate. This approach is compared with existing methods for segmentation and classification of road images.