Vehicle segmentation using evidential reasoning

This paper proposes a segmentation algorithm by means of an evidential reasoning to segment moving vehicles in front of our moving car in a road traffic scene. Generally, an evidential reasoning finds the perceptually known evidences of a target and updates a probabilistic expectation for the target to be in an image. Since a noise image produces unreliable features and degrades the detection and localization, selecting image primitives which are less sensitive to noise and well represent the evidences is important. We carry out this task by the probabilistic integration of image features based on maximum a posteriori (MAP) probability that combines the prior and likelihood probabilities using Bayes' rule.