Abstract: A computer-implemented depth estimation method based on non-parametric Census transform with adaptive window patterns and semi-global optimization. A modified cross-based cost aggregation technique adaptively creates the shape of the cross for each pixel distinctly. In addition, a depth refinement algorithm fills holes within the estimated depth map using the surrounding background depth pixels and sharpens the object boundaries by exerting a trilateral filter to the generated depth map. The trilateral filter uses the curvature of pixels as well as texture and depth information to sharpen the edges.

Abstract: A region of interest (ROI) generation method for stereo-based pedestrian detection systems. A vertical gradient of a clustered depth map is used to find ground plane and variable-sized bounding boxes are extracted on a boundary of the ground plane as ROIs. The ROIs are then classified into pedestrian and non-pedestrian classes. Simulation results show the algorithm outperforms the existing monocular and stereo-based methods.

Abstract: A computer-implemented depth estimation method based on non-parametric Census transform with adaptive window patterns and semi-global optimization. A modified cross-based cost aggregation technique adaptively creates the shape of the cross for each pixel distinctly. In addition, a depth refinement algorithm fills holes within the estimated depth map using the surrounding background depth pixels and sharpens the object boundaries by exerting a trilateral filter to the generated depth map. The trilateral filter uses the curvature of pixels as well as texture and depth information to sharpen the edges.