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Horn-Schunck Global Optical Flow

MediumCV

Implement Horn-Schunck optical flow with smoothness constraint to estimate the motion of pixels between two consecutive frames. This method is used to calculate the optical flow, which is the apparent motion of pixels in an image.

The Horn-Schunck method assumes that the brightness constancy of the scene is maintained between frames, which can be expressed as $I_x u + I_y v + I_t = 0$ , where $I_x$ and $I_y$ are the spatial gradients of the image intensity, $I_t$ is the temporal gradient, and $u$ and $v$ are the horizontal and vertical components of the optical flow. To handle the aperture problem, a smoothness constraint is added to the energy function.

Here are the steps to calculate the optical flow:

Compute the spatial and temporal gradients of the image intensity.
Initialize the optical flow components $u$ and $v$ .
Iterate to minimize the energy function.

E = \int\int (I_x u + I_y v + I_t)^2 + \alpha(|\nabla u|^2 + |\nabla v|^2) dx dy

This technique is widely used in video analysis and object tracking applications.

Example:

Input:

Two frames

Output:

Dense optical flow

Reasoning:

Iteratively solve with smoothness regularization

Constraints:

Input frames: 2D numpy arrays (grayscale) with shape (H, W) and dtype float32
Pixel values: in range [0, 255] and assumed to be normalized to [0, 1] for computation
Output optical flow: 2D numpy array with shape (H, W, 2) and dtype float32, representing horizontal and vertical flow components
Smoothness parameter: α is a positive scalar, assumed to be provided as an input or set to a default value of 0.001
Iteration limit: maximum number of iterations to minimize the energy function, assumed to be set to a default value of 1000 or provided as an input

Editor

Python 3.13.1

Test Results

0/0

Run code to see test results.