Study of particle tracking algorithms based on neural networks for stereoscopic tracking velocimetry

Stereoscopic-tracking velocimetry can offer an excellent potential for continuously monitoring three-dimensional flow fields for all three-component velocities in near-real-time. Requiring only the deployment of two solid-state cameras with directional freedom in test-section illumination and observation, the system can be built to be compact and robust. For flow velocimetry measurement, increasing the number density in particle seeding is much desirable for maximizing spatial resolution, that is, number of velocity data points of the captured field. The challenge, however, is how to successfully track numerous crisscrossing particles with speed and reliability. In our approach, the task of particle tracking is converted to an optimization problem for which neural networks are applied. Here we present the design and development of the neural networks for particle tracking as well as the investigation results based on both computer simulations and real experiments. The approach appears to be appropriate for near-real-time velocity monitoring, being able to provide reliable solutions achieved by the massive parallel-processing power of the neural networks.