Performance analysis of a new road following algorithm based on color models

Abstract

This paper describes and evaluates a vision system that accurately segments unstructured, non-homogeneous roads of arbitrary shape under various lighting conditions. The idea behind the road following algorithm is the segmentation of road from background through the use of color models. Data are collected from a video camera mounted on a moving vehicle. In each frame, color models of the road and background are constructed. The color models are used to calculate the probability that each pixel in a frame is a member of the road class. Temporal fusion of these road probabilities helps to stabilize the models, resulting in a probability map that can be thresholded to determine areas of road and non-road. Performance evaluation follows the approach described in Hong et al1. We evaluate the algorithm's performance with annotated frames of video data. This allows us to compute the false positive and false negative ratios. False positives refer to non-road areas in the image that were classified by the system as road, while false negatives refer to road areas classified as non-road. We use the sum of false positives and false negatives as an overall classification error calculated for each frame of the video sequence. After the error is calculated for each frame, we determine the statistics of the classification error throughout the whole video sequence. The overall classification error per frame allows us to compare the performance of several algorithms on the same frame, and we can analyze the overall performance of individual algorithms using their classification statistics.