摘要
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 algorithmexhibits outstanding real-time matching performance and accuracy,effectivelymeeting the feature-matching requirements of automotive panoramic surround view systems.
基金
the National Natural Science Foundation of China(61803206)
the Key R&D Program of Jiangsu Province(BE2022053-2)
the Nanjing Forestry University Youth Science and Technology Innovation Fund(CX2018004)for partly funding this project.
