Editing Optical flow (section)

== Uses ==

[[Motion estimation]] and [[video compression]] have developed as a major aspect of optical flow research. While the optical flow field is superficially similar to a dense motion field derived from the techniques of motion estimation, optical flow is the study of not only the determination of the optical flow field itself, but also of its use in estimating the three-dimensional nature and structure of the scene, as well as the 3D motion of objects and the observer relative to the scene, most of them using the image Jacobian.<ref>{{cite web|last=Corke|first=Peter|authorlink=Peter Corke|title=The Image Jacobian|url=https://robotacademy.net.au/lesson/the-image-jacobian/|website=QUT Robot Academy|date=8 May 2017}}</ref>

Optical flow was used by robotics researchers in many areas such as: [[object detection]] and tracking, image dominant plane extraction, movement detection, robot navigation and [[visual odometry]].<ref name="Kelson R. T. Aires, Andre M. Santana, Adelardo A. D. Medeiros 2008" /> Optical flow information has been recognized as being useful for controlling micro air vehicles.<ref>{{Cite journal |last1=Barrows |first1=G. L. |last2=Chahl |first2=J. S. |last3=Srinivasan |first3=M. V. |date=2003 |title=Biologically inspired visual sensing and flight control |journal=Aeronautical Journal |volume=107 |issue=1069 |pages=159–268 |doi=10.1017/S0001924000011891 |s2cid=108782688 |via=Cambridge University Press | url = https://www.cambridge.org/core/journals/aeronautical-journal/article/biologically-inspired-visual-sensing-and-flight-control/0B3884D11BB0A54C2A196BF57162C153}}</ref>

The application of optical flow includes the problem of inferring not only the motion of the observer and objects in the scene, but also the [[structure]] of objects and the environment. Since awareness of motion and the generation of mental maps of the structure of our environment are critical components of animal (and human) [[Visual perception|vision]], the conversion of this innate ability to a computer capability is similarly crucial in the field of [[machine vision]].<ref>{{Cite book |url={{google books|plainurl=yes|id=c97huisjZYYC|pg=PA133|text=optic+flow}} |title=Advances in Computer Vision |last=Brown |first=Christopher M. |publisher=Lawrence Erlbaum Associates |year=1987 |isbn=978-0-89859-648-9}}</ref>

[[Image:Optical flow example v2.png|thumb|right|The optical flow vector of a moving object in a video sequence.]]

Consider a five-frame clip of a ball moving from the bottom left of a field of vision, to the top right. Motion estimation techniques can determine that on a two dimensional plane the ball is moving up and to the right and vectors describing this motion can be extracted from the sequence of frames. For the purposes of video compression (e.g., [[MPEG]]), the sequence is now described as well as it needs to be. However, in the field of machine vision, the question of whether the ball is moving to the right or if the observer is moving to the left is unknowable yet critical information. Not even if a static, patterned background were present in the five frames, could we confidently state that the ball was moving to the right, because the pattern might have an infinite distance to the observer.