River video flow measurement algorithm with space-time image fusion of object tracking and statistical characteristics

Abstract

River flow monitoring serves as a crucial data source in hydrological stations, employing the widely adopted space-time image velocimetry (STIV) method both domestically and internationally. However, current methods yield space-time (ST) image trajectories with limited usable information, particularly due to the interference of complex flow characteristics and harsh outdoor environments in natural rivers. This interference leads to errors in detecting the main orientation of texture, consequently reducing measurement accuracy. To overcome these challenges, this paper proposes an river video flow measurement algorithm with ST image fusion of object tracking and statistical characteristics. The Lucas–Kanade tracking algorithm is employed to track velocity points and generate tracking trajectories. These trajectories’ coordinates are then fitted into straight lines to automatically arrange velocity-measuring lines in natural rivers. The algorithm leverages multi-scale detail boosting and Gaussian directional stretch filtering to enhance the quality and texture trajectories of ST images. Furthermore, background noise filtering and stripe noise filtering are applied to denoise the ST images, effectively mitigating the impact of noise caused by the natural river environment and camera equipment. The proposed algorithm incorporates projection statistical characteristics algorithms to accurately detect the dominant texture direction, enabling precise calculation of mean velocity and cross-sectional discharge of the river. Experimental validation was conducted in both natural and regular river channels, demonstrating the superior performance of the proposed solution compared to widely used large-scale particle image velocimetry, STIV, MobileNet-STIV and FD-DIS-G algorithms. The algorithm exhibited higher accuracy, stability, and applicability, with errors at each velocity-measuring point below 10%, and relative errors of the calculated mean velocity and cross-sectional discharge below 3%. This affirms the algorithm’s superior accuracy, stability, and applicability.