@Article{csse.2024.050817,
AUTHOR = {Guangbing Xiao, Ruijie Gu, Ning Sun, Yong Zhang},
TITLE = {Research on Feature Matching Optimization Algorithm for Automotive Panoramic Surround View System},
JOURNAL = {Computer Systems Science and Engineering},
VOLUME = {48},
YEAR = {2024},
NUMBER = {5},
PAGES = {1329--1348},
URL = {http://www.techscience.com/csse/v48n5/57940},
ISSN = {},
ABSTRACT = {In response to the challenges posed by insufficient real-time performance and suboptimal matching accuracy of traditional feature matching algorithms within automotive panoramic surround view systems, this paper has proposed a high-performance dimension reduction parallel matching algorithm that integrates Principal Component Analysis (PCA) and Dual-Heap Filtering (DHF). The algorithm employs PCA to map the feature points into the lower-dimensional space and employs the square of Euclidean distance for feature matching, which significantly reduces computational complexity. To ensure the accuracy of feature matching, the algorithm utilizes Dual-Heap Filtering to filter and refine matched point pairs. To further enhance matching speed and make optimal use of computational resources, the algorithm introduces a multi-core parallel matching strategy, greatly elevating the efficiency of feature matching. Compared to Scale-Invariant Feature Transform (SIFT) and Speeded Up Robust Features (SURF), the proposed algorithm reduces matching time by 77% to 80% and concurrently enhances matching accuracy by 5% to 15%. Experimental results demonstrate that the proposed algorithm exhibits outstanding real-time matching performance and accuracy, effectively meeting the feature-matching requirements of automotive panoramic surround view systems.},
DOI = {10.32604/csse.2024.050817}
}