## 3 Fast Optical Tracking Velocimetry  
Due to its relevant practical applications, surface flow monitoring has been deeply investigated in the past few years, and a recent trend proposes to tackle this task leveraging image feature correspondences. In this field, the OTV algorithm [1] relied on the pyramidal Lucas-Kanade algorithm [7] to track efficiently lightweight image features, such as FAST [12], extracted from consecutive frames of a video stream, as depicted in Figure 2.  
Specifically, given two consecutive image frames of a video sequence at time \(t\) and \(t+1\), it extracts relevant features (e.g., FAST [12] from them and searches for corresponding ones through the Lucas-Kanade optical-flow algorithm [7]. The outcome of this process is then processed to detect inconsistent trajectories according to priors about the expected rough river flow direction [1].  
Despite its simplicity, OTV turned out quite effective compared to other methods known in the literature and much less computationally demanding. This latter feature, coupled with additional algorithmic considerations and taking advantage of accelerated computing capabilities available nowadays in almost every microprocessor systems, enabled its deployment even on low power embedded devices [3]. Figure 3 shows the outcome of the OTV algorithm processing video sequences acquired on the Tevere and Secchia rivers in Italy. The latter is particularly challenging since it frames the Secchia river during varying lighting conditions, probably originated by clouds, strongly affecting the image acquisition process. Moreover, there is also an additional perturbation generated by small waves opposite to the streamflow. Despite these multiple perturbations, OTV can reliably track a sufficient amount of features to infer streamflow behavior, as depicted in the rightmost image at the bottom of Figure 3. Nonetheless, deeper perturbations as rain and more unsatisfactory lighting conditions might degrade the capacity of OTV (as well as those of all the image velocimetry methods) to infer meaningful streamflow parameters. Although this limitation may be relevant in some cases, its analysis is out of the scope of this work. The OTV achievements potentially pave the way for surface flow monitoring at the edge. However, enabling such a task in situ, especially remote ones, requires crossing an additional hurdle concerning self-powering the overall measurement system. Purposely, in the following sections, we deeply investigate this issue using cheap solutions readily available on the market.  
Moreover, we propose an additional optional simplification to speed up the measurement process further and potentially save energy. It simply consists of replacing color video streams with monochrome ones to reduce the most computationally intensive phase of the whole measurement process, the OTV task. As reported in the experimental results section, such a simple modification, processing images at Full and Half resolution, achieves performance substantially on-par with the original OTV approach but at a significantly faster rate, consequently draining less energy.  
Figure 2: Description of the OTV algorithm [1].