This study focuses on utilizing image techniques for river velocity measurement, with a specific emphasis onnatural surface floating patterns. Employing a multi-camera system, we conducted 3D measurements on riversurfaces, including surface velocity and water surface reconstruction. A pattern-based tracking approach hasbeen adopted to improve the performance of image measurements on different types of natural floating tracers.The study employs the following approaches: 3D Lagrangian Pattern Tracking Velocimetry (3D-LPTV), 2DLagrangian Pattern Velocimetry (2D- LPTV), and Large-scale Particle Image Velocimetry (LSPIV), for surfacevelocity estimation. The outcomes revealed that all three approaches yielded consistent results in terms ofaveraged velocity. However, the LSPIV method produced about two times higher uncertainty in measured velocitiescompared to the other methods. A strategy to assess the quality of river surface patterns in velocityestimation is presented. Specifically, the sum of squared interrogation area intensity gradient (SSIAIG) was foundto be strongly correlated with measurement uncertainty. Additionally, a term related to the peak sidelobe ratio(PSR) of the cross-correlation map was found as an effective constraint, ensuring the image-tracking processachieves high reliability. The precision of measurements increases corresponding to the increase of image intensitygradient and PSR.