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Lots of resources say that there are two types optical flow algorithms. And Lucas-Kanade is a sparse technique, but I can't find the meanings of sparse and dense? Can some one tell me what is the difference between dense and sparse optical flow?
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Optical flow is defined as the apparent motion of individual pixels on the image plane. It often serves as a good approximation of the true physical motion projected onto the image plane.
Optical flow is a vector field between two images, showing how the pixels of an object in the first image can be moved to form the same object in the second image . It is a kind of correspondence learning, because if the corresponding pixels of an object are known, the optical flow field can be calculated.
Optical flow is a technique used to describe image motion. It is usually applied to a series of images that have a small time step between them, for example, video frames. Optical flow calculates a velocity for points within the images, and provides an estimation of where points could be in the next image sequence.
Description. opticFlow = opticalFlowFarneback returns an optical flow object that you can use to estimate the direction and speed of the moving objects in a video. The optical flow is estimated using the Farneback method.
The short explanation is, sparse techniques only need to process some pixels from the whole image, dense techniques process all the pixels. Dense techniques are slower but can be more accurate, but in my experience Lucas-Kanade accuracy might be enough for real-time applications. An example of a dense optical flow algorithm (the most popular) is Gunner Farneback's Optical Flow.
To get an overview of the flow quality look at the benchmark page e.g. the KITTI or the Middleburry dataset
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Sparse optical flow gives you the flow vectors of some "interesting features" within the image.
Dense optical flow attempts to give you the flow all over the image - up to a flow vector per pixel.
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