Crowd-based velocimetry for surface flows

Flow velocity measurement is important in hydrology. Recently, owing to the popularity of sensors and processors, image-based flow velocity measurement methods have become an important research direction. Particle image velocimetry (PIV) is a key example. However, due to the uncertainty of the features, PIV sometimes provides very inaccurate results and always requires customized setups. In this research, we take advantage of the human perception system, that is, the strong abilities related to feature identification and tracking, in order to estimate the surface flow velocity of a river. We developed a method called crowd-based velocimetry (CBV) to incorporate the human perception capacity in the estimation of the flow velocity. CBV includes three main steps: (1) video processing, (2) crowd processing, and (3) statistical processing. We validated CBV by measuring a fast, steady, and uniform river surface flow in an artificial canal. The results show that compared to radar measurements from the center of the flow, CBV measured the surface flow velocity with a deviation ranging between +12.1% and +17.3% from the radar measurement, while PIV resulted in a 1.7% to 24.3% deviation. With rapidly improving mobile devices, CBV allows enormous numbers of people to engage in flow measurement, making CBV more reliable, more efficient, and more economical.