A stereo matching method applied in blind spot detection of the field of view

Mei Jin; Rui Zhang; Liguo Zhang

doi:10.1117/12.3033160

5 July 2024 A stereo matching method applied in blind spot detection of the field of view

Mei Jin, Rui Zhang, Liguo Zhang

Proceedings Volume 13184, Third International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024); 131841Q (2024) https://doi.org/10.1117/12.3033160
Event: 3rd International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024), 2024, Kuala Lumpur, Malaysia

Abstract

The issue of blind spots in the field of vision during automobile driving has always been a significant concern. In response to the limitations of the stereo-matching algorithm based on census transforms, such as inadequate accuracy, insensitivity to textures, and excessive reliance on central grayscale values, an improved local stereo-matching algorithm based on census transform is proposed. The algorithm utilizes the maximum cross-domain variance method to calculate the matching window size, reducing mismatches in discontinuous disparity regions. The minimum cross-domain mean method is employed to determine the grayscale value of the central pixel, minimizing external influences on the central pixel. Additionally, a guided filter with edge-preserving characteristics is used for cost aggregation, further reducing mismatches in non-continuous domains. The experimental results illustrate that the introduced algorithm attains an average mismatch rate of 5.96% on the Middlebury benchmark dataset, marking a substantial enhancement when contrasted with the original census algorithm, which exhibited a mismatch rate of 16.2%.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Mei Jin, Rui Zhang, and Liguo Zhang "A stereo matching method applied in blind spot detection of the field of view", Proc. SPIE 13184, Third International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024), 131841Q (5 July 2024); https://doi.org/10.1117/12.3033160

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