Motion is one of the most important features describing an image sequence. Motion estimation has been widely applied in structure from motion, vision-based navigation and many other fields. However, real-time motion estimation remains a challenge because of its high computational expense. The traditional CPU-based scheme cannot satisfy the power, size and computation requirements in many applications.
With the availability of new parallel architectures such as field-programmable gate arrays (FPGAs) and global processing units (GPUs), applying these new technologies to computer vision tasks such as motion estimation has been an active research field in recent years. FPGAs have been applied to real-time motion estimation for their outstanding properties in computation power, size, power consumption and reconfigurability.
This technology allows unmanned aerial vehicles (UAVs) to automatically detect obstacles in flight on a real-time basis without ground user direction. UAVs can be programmed with a specific flight path and then can adjust the flight path according to obstacles that it finds along that path, and still end up at the desired location.
For more information, contact Dee Anderson, Associate Director, BYU Technology Transfer Office, 801-422-3676.
