Pixel characteristics based feature extraction approach for roadside object detection

Classification of roadside objects is very important task in identifying fire risk regions, analysing roadside conditions and improving roadside safety. This paper introduces a novel and effective way to detect soil, grass, road and tree from roadside images thus giving a better decision-making system for analysing roadside video data. A new feature extraction approach is proposed to detect and classify the roadside objects. Feature set is based on colour characteristics which are obtained by analysing components of image pixels. Choosing an appropriate feature set is one of the great challenges for successful identification of roadside objects. Based on the proposed feature set and the Support Vector Machine, the detection and classification approach is implemented. The proposed approach is evaluated using the training and test data from real-world roadside video images. The results show that the proposed approach is able to accurately detect grass, soil, road and tree.