Optical Flow estimation in pure Python

In this repository, we deal with the task of video frame interpolation with estimated optical flow. To estimate the optical flow we use Lucas-Kanade algorithm, Multiscale Lucas-Kanade algorithm (with iterative tuning), and Discrete Horn-Schunk algorithm. We explore the interpolation performance on Spheres dataset and Corridor dataset.

Determining optical flow by using Horn-Schunck method and Lucas-Kanade method

Consist of four different approaches for generating optical flow and can be demonstrated in Colab.

An implementation of optical flow using both the Lucas Kanade method as well as Horn Schunck.

Digital Video Processing Graduate Course Homeworks

They are optical flow implementations by Lucas-Kanade and Horn–Schunck respectively.

Robert Barron optical flow code: slightest modifications for modern computers

Methods for estimating optical flow

Working on five computer vision tasks (optical flow, mean-shift tracking, correlation filter tracking, advanced tracking, and long-term tracking) using the programming language Python.

Implementation of the two most well known optical flow estimation methods, the Lucas-Kanade method and the Horn-Schunck method.

Optical flow - Lucas Kanade - Horn & Schunck

Optical flow global motion calculation

Implementation of Lucas-Kanade and Horn-Schunck methods for optical flow

Create a 3D points cloud with Horn Schunck algorithm

Motion detection using Horn-Schunck method for optical flow estimation

Optical Flow Transfer

Contains crude computer vision techniques with less emphasis on Deep learning

Optical flow with Horn-Schunck method using C++

Estimation De mouvement (Methode de Horn et shunck) using C language