Object Recognition with Class Conditional Gaussian Mixture Model - A Statistical Learning Approach

Object recognition is one of the key tasks in robot vision. In RoboCup SPL, the Nao Robot must identify objects of interest such as the ball, field features et al. These objects are critical for the robot players to successfully play soccer games. We propose a new statistical learning method, Class Conditional Gaussian Mixture Model (ccGMM), that can be used either as an object detector or a false positive discriminator. It is able to achieve a high recall rate and a low false positive rate. The proposed model has low computational cost on a mobile robot and the learning process takes a relatively short time, so that it is suitable for real robot competition play.