Rip currents are like rivers of fast-moving water that can quickly carry the unwary out to sea. They are not easy to recognize, especially to the untrained observer. Other than in-situ current measurements, there exists a number of methods that analyzes images and videos to detect rip currents. Most of these techniques base their detection on the appearance of rip currents such as foamy pattern, discoloration of water, and locations of breaking waves. Leading methods use either image processing or machine learning of images and/or video input. In this paper, we analyze the behavior of water movement rather than simply its appearance to detect rip currents. Specifically, we investigated several flow visualization methods and tune them to detect rip currents. Based on our study, we recommend two methods that allowed us to detect rip currents where other methods have failed. And because the methods originated as visualization techniques, any presence of rip currents are automatically highlighted. We also evaluated these two methods against previously annotated results by rip current experts, and found that our detections were sufficiently sensitive that some expert annotations were relabeled.