A color feature learning and robust interpretation of moving object using HMM
Hidehiro Ohki, Takamasa Hori, Keiji Gyohten, Shinji Shigeno · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2004
Spot observation by computer vision is the one of fundamental key technology. In this paper, we propose a moving object color learning and robust recognition with Hidden Markov Model(HMM) from various scenes under different light conditions. Feature box which is a small area in a image is defined to observe a spot. The time series data of such as averages of R, G, B intensities in feature boxes are the input signals of our system. The HMMs learn correspondences of input signals with object color of moving object and background. Baum-Welch and Vi-terbi algorithms are used to learning and interpret the spot scene transition. In moving object color interpretation, the system selects a best HMM model for input signals using maximum likelihood method based on a given object color appearance grammar. In the experiment, we examine the number of feature boxes and its shapes under some light conditions. The feature boxes adjoining in vertical column whose height is almost same as objects results best score in the experiment. It shows the effectiveness of our method.