Real-time disparity estimation using foreground segmentation for stereo sequences

Abstract

We propose a fast disparity estimation algorithm using background registration and object segmentation for stereo sequences from fixed cameras. Dense background disparity information is calculated in an initialization step, so that only disparities of moving object regions are updated in the main process. We propose a real-time segmentation technique using background subtraction and interframe differences, and a hierarchical disparity estimation using a region-dividing technique and shape-adaptive matching windows. Experimental results show that the proposed algorithm provides accurate disparity vector fields with an average processing speed of 15 frames/s for 320×240 stereo sequences on an ordinary PC.