PRESCAN Adaptive Vehicle Image Real-Time Stitching Algorithm Based on Improved SIFT

Abstract

Aiming at the problems of high latency, low accuracy, and obvious stitching gaps of traditional image stitching techniques in PRESCAN, this paper proposes an adaptive real-time vehicle image stitching algorithm based on improved scale-invariant feature transform (SIFT). Firstly, a multi-threaded structure is utilized to combine the improved adaptive features from accelerated segment test algorithm with SIFT descriptors for feature extraction. Secondly, a mismatching filtering strategy based on the BF algorithm has been introduced. Finally, image stitching is performed by combining random sample consensus algorithm and adaptive region stitching strategy. The results show that the recall rate is about 30.64%, the accuracy of matching is about 98.86%, and the total stitching time is about 22.82ms. It effectively improves the quality of feature point extraction and matching accuracy and significantly reduces the stitching time of images. It provides a real-time and accurate vehicle image stitching method for the field of perception in the autonomous driving laboratory environment.