What is Optical Flow?

The namesake for this blog is “Opticalflow” — in fact, an example of “dense optical flow” is seen in the two graphics at top.  An excellent visual example in video form can be seen below that will give you a good feel for it (the good folks at gpu4vision.org are not affiliated with me nor my company, but they have a great demo):

Performing dense, real-time computation of optical flow tells you three things, about any two given video frames: what pixels moved, how much they moved, and exactly what direction.   The video shown above shows the direction coded in the color, and the magnitude of the motion in the brightness.

This is a lot like motion estimation, as used for video encoding — but unlike most motion estimation, it is performed for every pixel (dense) and at very high subpixel accuracy (which differentiates motion estimation and optical flow). What is this useful for?

Accurate, real-time optical flow has widely been viewed as “the Holy Grail” of image processing, because it allows very advanced and accurate algorithms for robotics and computer vision, advanced video scaling and superresolution (think CSI, where the forensic technician takes blurry and noisy security camera video and magically cleans it up so they can read a license plate).  An example of this kind of application is for automated UAV and MAV navigation.  MAV’s are Micro Air Vehicles — think of UAVs, except the size of a prehistoric dragonfly.  Lance Winslow at DIY Drones has a very interesting post about using kites or mobiles to confuse the optical flow algorithms in UAV/MAV systems:

http://diydrones.com/profiles/blogs/confusing-mav-and-uav-optical

A key area of research in optical flow algorithms is how to make them robust to wierd phenomenon like this — not just because the real world is strange when viewed through a camera, but television programming is even more strange.

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~ by opticalflow on February 9, 2009.

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