Detection and Segmentation of Moving Objects from Dynamic RGB and Depth Images

Abstract

This paper proposes a method that combines Temporal Modified-RANSACTMR with a fixation-based segmentation algorithm for reconstructing the 3D structure of moving and still objects that are tracked in video and depth image sequences acquired by moving Kinect© and/or range finders First, we compute 3D optical flow of feature points. Second, TMR classifies all flows into consistent 3D flow sets for the background and each moving object; simultaneously, the rotation matrix and the translation vector for each 3D flow set are computed. Then, an improved fixation-based segmentation method segments each object's area. Finally, dense 3D models for the background and each moving object are constructed along with each object's rotation matrix and translation vector in each frame. Experiments using multiple moving objects in color and depth image sequences acquired by Kinectc demonstrate the effectiveness of our proposed method.