## 1 Introduction  
The application of video analysis has recently become one of the leading interdisciplinary trends in modern aquaculture. Some of the evident reasons for this situation are the increasingly available and affordable video cameras as well as the growth of the computational power of the popular devices that, in some applications, may replace typical personal computers, such as, e.g., Raspberry Pi, FPGA boards, nVidia Jetson Nano and similar more sophisticated hardware platforms. An analogous situation also exists in the automotive industry and some other areas of technology, which is typical for the rapid development of Industry 4.0 solutions, which is becoming Agriculture 4.0 as well [1]. The most typical applications of video analysis in fish farming seem to be motion tracking [2], as well as the recognition and classification of fish [3], which has recently also utilized popular convolutional neural networks (CNNs) [4]. The application of such video systems may also be helpful for the determination of fish behavior [5; 6; 7; 8] as well as in the development of intelligent feeding systems [9; 10; 11]. Another interesting area of research related to these topics is water-quality monitoring [12]. Some other recent works focus on the applications of artificial-intelligence methods, including mobile remote-control systems and IoT solutions applied in fish farms [13; 14]. Many of these research areas are motivated by the willingness to limit the number of sensors and additional equipment mounted inside the ponds or tanks, which is also essential for economic reasons. Such video-based approaches do not affect fish directly, reducing the possibility of injuring individual fish, additionally simplifying the hardware architecture of systems containing sensors and their necessary connections. Therefore, a similar approach may also be beneficiary in monitoring the water flow in recirculating aquaculture systems (RAS). Proper water flow in fish-farming ponds and tanks is one of the most crucial requirements in the fish-farming centers in which species such as salmon and trout are bred. These fish behave in a specific way, positioning themselves parallel to the water current. Therefore, the correct flow velocity of water determines the proper development of the fish as well as the welfare of the whole group of them present in the pond. Much work has been carried out in recent years to determine the optimal flow speeds for maintaining overall fish health, but the water velocity distribution inside circular tanks is often very heterogeneous, particularly near their walls (edges). Usually, these works are focused on analyzing the effect of design parameters on the distribution of water velocities inside circular aquaculture tanks [15]. An example of such a model can be found in the paper [16], where the proposed model estimates the velocity distribution by determining the angular momentum per unitary mass close to the tank wall and around the central axis. The model depends on the water inlet and outlet flow rates, water inlet velocity, reservoir radius, water depth, and three reservoir-specific parameters that must be determined experimentally. It also takes into account the influence of the wall roughness, the characteristics of the water inlet devices, and the presence of individual elements at the bottom of the tank causing friction losses. However, it is not always necessary to use such advanced models, as in most cases simplified models of flow distribution are sufficient. The optical flow methods analyzed in this paper may also be used for the estimation of the water-flow velocity in rivers [17; 18; 19], replacing previously used particle image velocimetry (PIV) methods [20] as well as typical motion-tracking methods based on point detectors [21]. Nevertheless, in such types of video recordings, there is no ground truth data; hence, the evaluation of the obtained results may be conducted with the use of trajectory reconstruction.