Integrating intensity and texture differences for robust change detection

We propose a novel technique for robust change detection based upon the integration of intensity and texture differences between two frames. A new accurate texture difference measure based on the relations between gradient vectors is proposed. The mathematical analysis shows that the measure is robust with respect to noise and illumination changes. Two ways to integrate the intensity and texture differences have been developed. The first combines the two measures adaptively according to the weightage of texture evidence, while the second does it optimally with additional constraint of smoothness. The parameters of the algorithm are selected automatically based on a statistic analysis. An algorithm is developed for fast implementation. The computational complexity analysis indicates that the proposed technique can run in real-time. The experiment results are evaluated both visually and quantitatively. They show that by exploiting both intensity and texture differences for change detection, one can obtain much better segmentation results than using the intensity or structure difference alone.