Course project for CSE 344 : Computer Vision.
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Updated
Apr 18, 2017 - Python
Course project for CSE 344 : Computer Vision.
An implementation of optical flow using both the Lucas Kanade method as well as Horn Schunck.
Lucas Kanade optical flow generator
Problem Set solutions for the "Introduction to Computer Vision (ud810)" MOOC from Udacity
This script is a dense modification of the Lucas Kanade Optical flow that is implemented in OpenCV sparsely.
Lucas-Kanade Template Tracker
Taking a Deeper Look at the Inverse Compositional Algorithm (CVPR 2019, Oral)
The swiss army knife for extracting optical flow
An implementation of several tracking algorithms based on Lucas Kanade algorithms
Implementation of Lucas-Kanade tracker algorithm to track a moving car, face of a baby and running Usain Bolt
They are optical flow implementations by Lucas-Kanade and Horn–Schunck respectively.
Object tracking using Lucas-Kanade template tracking
Consist of four different approaches for generating optical flow and can be demonstrated in Colab.
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.
Computer Vision CS ( 6476)
A generic pipeline to align multimodal image pairs from different sensors by extending Lucas-Kanade on feature maps. CVPR2021
Here we try to track the motion of the vehicles on a highway using the concept of Optical Motion Flow.
Optical Flow estimation in pure Python
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