Open Access

ARTICLE

Research on Feature Matching Optimization Algorithm for Automotive Panoramic Surround View System

Guangbing Xiao^*, Ruijie Gu, Ning Sun, Yong Zhang

College of Automobile and Traffic Engineering, Nanjing Forestry University, Nanjing, 210037, China

* Corresponding Author: Guangbing Xiao. Email:

Computer Systems Science and Engineering 2024, 48(5), 1329-1348. https://doi.org/10.32604/csse.2024.050817

Received 19 February 2024; Accepted 13 May 2024; Issue published 13 September 2024

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.

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Keywords

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