Rip current and channel detection using surfcams and optical flow

Abstract

Rip currents are a common, naturally occurring surf-zone hazard that pose a risk to beach patrons. This study presents a remote-sensing-based algorithm to detect rip currents and rip channels. Optical flow-based computer vision methods are implemented to analyze large data sets and the automatic detection of these features. Surfcam video was collected from dissipative (La Jolla, CA), intermediate (Long Beach, NY), and reflective beaches (Pensacola Beach, FL) to demonstrate the efficacy of the methods. A clustering technique using the dominant wave period was implemented to transition from detected offshore movements to rip currents. The methods presented in this paper were used to detect 20,327 rip currents and 1,100 rip channels. The average accuracy for rip current and rip channel detection was 67.3% and 96.2%, respectively. The remote-sensing-based detection methods can be adapted for use on other video-based equipment and, with additional modifications, can be implemented in an operational capacity.