1 / 22

Optical Flow

Optical Flow. Brian Renzenbrink. What is Optical Flow?. Optical flow is the distribution of apparent velocities for movement of brightness patterns in a sequence of images. Optical flow can be a result of relative motion of objects in a scene or of motion on the viewer's side.

urian
Télécharger la présentation

Optical Flow

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Optical Flow Brian Renzenbrink

  2. What is Optical Flow? Optical flow is the distribution of apparent velocities for movement of brightness patterns in a sequence of images. Optical flow can be a result of relative motion of objects in a scene or of motion on the viewer's side. What does that mean?

  3. Optical Flow Constraints Optical flow is a constrained problem that only guarantees results under certain conditions. • The Smoothness Constraint requires that neighboring points in the image have similar velocities in the velocity field. • This means discontinuities represent object occlusion. • The Minimization Constraint requires that as our expected noise trends toward zero our error rate for our derivative must be tend towards zero as well • This means that our derivative estimates have to be accurate. Well that sucks.

  4. Examples in Action Here are two examples of optical flow diagrams: Optical flow of a sink Optical flow of a camera rotation

  5. Examples in Action Here are two examples of optical flow diagrams: Optical flow of a sink Optical flow of a camera rotation

  6. Examples in Action And a video of my implementation:

  7. Types of Optical Flow Algorithms There are several types/classes of optical flow algorithms, each with their own advantages • Intensity-based Differential Methods • Frequency-based Methods • Correlation-based Methods • Multiple Motion Methods

  8. Intensity-based Differential Methods • Differential techniques compute image velocity from the spatiotemporal derivatives of image intensities • They assume the image is continuous and differentiable in space and time • These methods use the Smoothness Constraint to regulate the image and identify dense optical flows over large images. • This method is the most popular and has the largest number of unique implementations • Horn and Schunk's method is the classic example

  9. Frequency-based Methods • This class of optical flow techniques is based around the use of velocity-tuned filters. • Orientation sensitive filters in the Fourier domain of time varying images allow for these methods to estimate motion in image signals that difficult to capture outside of Fourier space, • The outputs of quadruple pairs from these filters can be squared and summed to give an approximation of motion energy. • Useful for seemingly random motion or flickering type noise • These methods are able to ignore the Minimization Constraint due to the inclusion of noise in the calculations

  10. Correlation-based Methods • Correlation-based methods define displacement between two images as the shift that yields the best fit between contiguous time-varying regions. • The basic concept is to identify a shared region between the two images and use parametric algorithms to identify the highest probability shift that would go from one image to another. • Very useful in low frame count, high noise sequences • Accuracy is not as good as Intensity-based differential methods

  11. Multiple Motion Methods • Occlusion and transparency are two phenomena that cause multiple image motions • Multiple motion methods attempt to deal with these • The two common approaches in this class are Line Processes and Mixed Velocity distributions, with the former being more prevalent • Line processes model the discontinuities in the intensity map and are able to ignore the Smoothness Constraint • Significantly more accurate, but much less efficient than Intensity-based differential methods. • Complexity of processes causes loss of robustness.

  12. Horn & Schunck Algorithm • Horn and Schunck method does not require color images, only intensity images. • To meet the Smoothness Constraint, Horn and Schunck requires the image to be smoothed across it's local neighborhood. From there, the image must be quantized to a fixed grid and a set of points (a matrix), with the intensity values being given a real number representation.

  13. Horn & Schunck cont. • After quantization of the image, the measured brightness Ei;j;k at the i-th row and the j-th column in the k-th image frame can be estimated.

  14. Horn & Schunck cont. • The Laplacians of the horizontal and vertical flow velocities for u and v can be calculated

  15. Horn & Schunck cont. • And finally calculating the flow at a point from the local average using u, v, Ex, Ey, and Et with an alpha value that represents the expected noise: • Further iterations can use the formula:

  16. Make it happen. In MATLAB, of course. • First, convert the images to grayscale and double • Create two zero arrays of the same size as your images. These will be your horizontal and vertical flow velocity matrices • Smooth both images using a gaussian filter • Estimate the partial derivatives Ex, Ey, and Et • Can do this with convolution instead of the ugly formula in equations 1, 2, & 3

  17. Make it happen cont. • After computing the derivatives, estimate the flow velocities in an iterative loop to refine your estimate • Again, we don’t have to use the ugly formula from equations 4&5, we can use convolution • Use an averaging kernel and to create the uBar and vBar values • You can now plot your optical flow diagram. YAY!

  18. Yosemite Fly Through These are two frames from a famous ‘Yosemite Fly Through’ animated sequence created by Lynn Quam

  19. Video Frames • These are five frames taken from a video ‘PeopleVan.avi’ available on the website. • Overlayed on top of each of these is their optical flow diagrams.

  20. Other Uses • Training data for neural networks

  21. Other Uses • Object Tracking • http://www.youtube.com/watch?v=1D93RmW_eN4 • http://www.youtube.com/watch?v=oCsdU7xGCpI • Interface • http://www.youtube.com/watch?v=Q3gT52sHDI4 • http://www.youtube.com/watch?v=ddNvNJXwYxU

  22. Questions?

More Related