This paper develops a novel approach for fluid flow tracking and analysis. Specifically, the proposed algorithm is able to detect the traveling waves, compute the wave parameters and determine controlling film flow parameters for a liquid film flowing over a rotating disk. The input to the algorithm is an easily acquired video data. It is shown that under single light illumination, it is possible to track the specular portion of the reflected light on the traveling wave. Hence, wavy films can be tracked, and fluid flow parameters can be computed. The fluid flow parameters include wave velocities, wave inclination angles, and distances between consecutive waves. Once the parameters are computed, their accuracy is analyzed and compared with the solutions of the mathematical models based on the Navier–Stokes equations. The mathematical model predicts wave characteristics based on directly measured controlling parameters, such as disk rotation speed and fluid flow rate. It is shown that the calculated parameter values coincide with the predicted ones. The average computed parameters are within 5–10% of the predicted values. Next, the developed approach is generalized to model-based recovery of fluid flow controlling parameters: the rotation speed and the fluid-flow rate. The search in space for model parameters is performed to minimize the error between the flow characteristics predicted by the fluid dynamics model (e.g. distance between waves, wave inclination angles) and parameters recovered from video data. Results demonstrate that the speed of a disk and the flow rate, when compared to the ground truth available from direct observation, are recovered with the error less than 10%.

Common benchmark data sets, standardized performance metrics, and baseline algorithms have demonstrated considerable impact on research and development in a variety of application domains. These resources provide both consumers and developers of technology with a common framework to objectively compare the performance of different algorithms and algorithmic improvements. In this paper, we present such a framework for evaluating object detection and tracking in video: specifically for face, text, and vehicle objects. This framework includes the source video data, ground-truth annotations (along with guidelines for annotation), performance metrics, evaluation protocols, and tools including scoring software and baseline algorithms. For each detection and tracking task and supported domain, we developed a 50-clip training set and a 50-clip test set. Each data clip is approximately 2.5 minutes long and has been completely spatially/temporally annotated at the I-frame level. Each task/domain, therefore, has an associated annotated corpus of approximately 450,000 frames. The scope of such annotation is unprecedented and was designed to begin to support the necessary quantities of data for robust machine learning approaches, as well as a statistically significant comparison of the performance of algorithms. The goal of this work was to systematically address the challenges of object detection and tracking through a common evaluation framework that permits a meaningful objective comparison of techniques, provides the research community with sufficient data for the exploration of automatic modeling techniques, encourages the incorporation of objective evaluation into the development process, and contributes useful lasting resources of a scale and magnitude that will prove to be extremely useful to the computer vision research community for years to come.