Optical flow global motion calculation

This repository contains an implementation of the Pyramidal Lucas-Kanade optical flow algorithm

A manually-implemented Lucas-Kanade object tracker used to track cars/helicopter landing site. (Yet to be completed)

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.

mini-projects for understanding fundamentals in computer vision

Estimating the velocity of a Moving Car using Various Approaches

Vehicle speed estimation using the optical flow algorithm from a mono camera(CCTV)

This repository contains a collection of solved exercises that implement various algorithms from the field of image processing, including the Lucas-Kanade algorithm, neural networks, and other advanced methods.

Implementation of the Lucas-Kanade pyramidal optical flow algorithm to register 3D medical images; 1st repo in a series of 3 repos associated with the research article "Prediction of the motion of chest internal points using an RNN trained with RTRL for latency compensation in lung cancer radiotherapy" (Pohl et al, Comput Med Imaging Graph, 2021)

Robert Barron optical flow code: slightest modifications for modern computers

MSc Thesis on Simultaneous Localization and Mapping (SLAM) in a single camera system

Computer Vision

Real-time automated object tracking applications for both template matching and motion detection

This project involves tracking keypoints using Lucas kanade feature tracking algorithm

OpenCV demonstrations of selected algorithms related to motion tracking: (1) mean shift, (2) CAMSHIFT, (3) iterative Lucas-Kanade algorithm with pyramids, and (4) Farnebäck's algorithm

This project was developed for the Computer Vision course of the Master Degree Artificial Intelligence Systems at the University of Trento.

💻 🧐 NTUA ECE Video & Image Analysis Source Codes

💻 🧐 NTUA ECE Computer Vision Source Codes

ECE NTUA Computer Vision

This repo includes solutions to all the 'in the class quizzes' and 7 problem sets of the Introduction to Computer Vision course (G Tech CS6476 - on Udacity)