River flow estimation using video data

Abstract

An image-based framework for river flow moni- toring based on a statistical estimation technique for fluid flow estimation is presented. This approach uses subsequent gray-scale video frames along with a statistical estimation method to extract the optical flow. An average velocity estimate is computed using the velocity vectors of the main motion trend, which is extracted using classification methods. The corresponding real-world sur- face velocity is computed using velocity-area transformations. The use of only two subsequent video frames and the lack of tracers in the flow are the key features of this technique in order to extract an accurate estimate of the real surface velocity. We compare our real-world surface velocity estimate with traditional current meter measurements, made on the site of Pinios river, Thessaly, Greece using the Q-liner 2 Doppler device 1 .