Haar Filter Hardware Architecture for the Accuracy Improvement of Stereo Vision Systems

In stereo vision systems, mismatching can occur frequently in specific regions containing noise and high-frequency components, such as checkered patterns. Therefore, this paper proposes a preprocessing method and architecture based on a 2-D Haar filter to improve the matching accuracy of disparity map by reducing the high-frequency and noise components. In this paper, the disparity map is computed by the semi-global matching (SGM) method, and separable weighted median filter is adopted as postprocessing. The SGM with proposed method reduces the average number of mismatching pixels by 3.5233% in non-occlusion condition and 3.2647% in occlusion condition compared to that with a 2-D Gaussian filter when using the KITTI 2015 stereo dataset. The proposed method is suitable for embedded stereo vision systems that requires high matching accuracy with reasonable resource overhead.