This study investigates the performance of two keypoint detection algorithms, SIFTand LoFTR, for vehicle re-recognition on a 2+1 road in Täby, utilizing three differentmethods: proportion of matches, ”gates” based on the values of the features andSupport Vector Machines (SVM). Data was collected from four strategically placedcameras, with a subset of the data manually annotated and divided into training,validation, and testing sets to minimize overfitting and ensure generalization. TheF1-score was used as the primary metric to evaluate the performance of the variousmethods. Results indicate that LoFTR outperforms SIFT across all methods, with theSVM method demonstrating the best performance and adaptability. The findings havepractical implications in security, traffic management, and intelligent transportationsystems, and suggest directions for future research in real-time implementation andgeneralization across varied camera placements.