The Lucas-Kanade method for optical flow estimation involves the following main steps:
Feature detection: Identify key features or points in the first frame that can be tracked across subsequent frames. These features are usually corners, edges, or blobs.
Feature tracking: Track the identified features across subsequent frames by finding their corresponding locations in each frame using a search window around the original point.
Formulating equations: Use a linear approximation to estimate the motion of each feature between consecutive frames. This involves formulating a set of linear equations based on the brightness constancy assumption that states that the intensity of a pixel does not change significantly across time.
Solving equations: Solve the set of linear equations using least squares optimization to obtain an estimate of the displacement vector for each feature.
Estimating global motion: Combine all individual feature displacements to obtain an estimate of the overall motion field for the image sequence.
Iterative refinement: Repeat steps 3-5 with updated estimates of motion until convergence is achieved.
Interpolation: Optional step where missing data points are filled in through interpolation techniques such as bilinear interpolation or spline fitting.
Visualization: Finally, visualize the obtained optical flow fields to analyze and interpret motion patterns in the image sequence.
