[Optical Flow/00] Introduction to Optical Flow
This post is written by YoungJ-Baek
You can see the original Korean version at Searching Fundamental
Preface
Optical flow is a fundamental problem in computer vision that has been studied for decades. It refers to the motion of objects in an image or video sequence, which can be estimated by analyzing the spatiotemporal changes of pixel intensities. Optical flow has a wide range of applications in robotics, autonomous driving, augmented reality, and video compression, to name a few.
Despite its long history and practical importance, optical flow remains a challenging task due to various factors such as occlusion, motion blur, illumination changes, and textureless regions. Over the years, numerous algorithms and techniques have been proposed to address these issues and improve the accuracy and robustness of optical flow estimation.
In this series, I will provide an introduction to optical flow from theory to practice. We will start by introducing the basic concepts and assumptions of optical flow, followed by a survey of classic and modern optical flow methods. We will also discuss the evaluation metrics and datasets commonly used for optical flow benchmarking. Finally, we will provide some practical tips and tricks for using optical flow in real-world applications.
Whether you are a beginner in computer vision or an experienced practitioner, I hope this series will help you gain a better understanding of optical flow and its potential in various fields.
Overview
In this series, we will explore optical flow from both theoretical and practical perspectives. Composed of two parts, the series will cover the basic concepts and assumptions of optical flow, classic and modern optical flow methods, evaluation metrics and datasets, and practical tips and tricks for using optical flow in real-world applications.
In the first part of the series, we will focus on the theoretical aspects of optical flow. We will delve into the traditional approaches and algorithms used for optical flow estimation, such as Lucas-Kanade, Horn-Schunck, and their variations. We will also explore the recent trend and research on optical flow, including deep learning-based methods and the use of optical flow in unsupervised learning.
In the second part, we will provide some hands-on examples and toy projects to help you apply optical flow in real-world scenarios. We will use popular libraries and frameworks such as OpenCV and PyTorch to implement optical flow algorithms and showcase their applications in tasks such as object tracking, motion analysis, and video stabilization.
Whether you are a beginner in computer vision or an experienced practitioner, this series will provide you with a comprehensive understanding of optical flow, its challenges, and its potential applications.
Index
Preface and Overview
Part 1.
(TBD)
Part 2.
(TBD)
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