Moving-Object Detection From Consecutive Stereo Pairs Using Slanted Plane Smoothing

Detecting moving objects is of great importance for autonomous unmanned vehicle systems, and a challenging task especially in complex dynamic environments. This paper proposes a novel approach for the detection of moving objects and the estimation of their motion states using consecutive stereo image pairs on mobile platforms. First, we use a variant of the semi-global matching algorithm to compute initial disparity maps. Second, assisted by the initial disparities, boundaries in the image segmentation produced by simple linear iterative clustering are classified into coplanar, hinge, and occlusion. Moving points are obtained during ego-motion estimation by a modified random sample consensus) algorithm without resorting to time-consuming dense optical flow. Finally, the moving objects are extracted by merging superpixels according to the boundary types and their movements. The proposed method is accelerated on the GPU at 20 frames per second. The data which we use for testing and benchmarking is released, thus completing similar data sets. It includes 812 image pairs and 924 moving objects with ground truth for better algorithms evaluation. Experimental results demonstrate that the proposed method achieves competitive results in terms of moving-object detection and their motion state estimation in challenging urban scenarios.