OpenCV implements the Lucas-Kanade sparse optical flow method via the `cv2.calcOpticalFlowPyrLK()` function. This method performs a least-squares fit on the motion of a 3x3 pixel neighborhood (9 points) around each target point, assuming small movement between consecutive frames to satisfy the brightness constancy constraint. The function accepts the previous frame, the next frame, and the tracking points from the previous frame, returning the updated points and a status vector indicating successful tracking.

Lucas-Kanade Optical Flow in OpenCV

The Lucas-Kanade sparse optical-flow method estimates the displacement of selected target points between consecutive frames. For each point, it uses a least-squares fit over the nine pixels in a local 3 × 3 neighborhood, assuming brightness constancy, small inter-frame motion, and similar motion among neighboring pixels.

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Optical Flow is the tracking of the motion of objects between two consecutive frames of a video. It relies on pixel intensities between frames being similar and neighboring pixels having similar motion. This allows it to be a fast implementation and useful for applications such as structure from motion, video stabilization, and video compression.

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