Adaptive texture and color segmentation for tracking moving objects

Abstract

Color segmentation is a very popular technique for real-time object tracking. However, even with adaptive color segmentation schemes, under varying environmental conditions in video sequences, the tracking tends to be unreliable. To overcome this problem, many multiple cue fusion techniques have been suggested. One of the cues that complements color nicely is texture. However, texture segmentation has not been used for object tracking mainly because of the computational complexity of texture segmentation. This paper presents a formulation for fusing texture and color in a manner that makes the segmentation reliable while keeping the computational cost low, with the goal of real-time target tracking. An autobinomial Gibbs Markov random field is used for modeling the texture and a 2D Gaussian distribution is used for modeling the color. This allows a probabilistic fusion of the texture and color cues and for adapting both the texture and color over time for target tracking. Experiments with both static images and dynamic image sequences establish the feasibility of the proposed approach.