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Category: OpenCV

#001 Advanced Computer Vision – Introduction to Direct Visual Tracking

Highlights: In this post, we will review some simple tracking methods. First, we will introduce several types of visual tracking methods. Then, we will explain how we can classify them. We will also talk about the fundamental aspects of direct visual tracking, with a special focus on region-based methods and gradient-based methods. In future post series, we will provide a detailed mathematical derivation of the Lucas Kanade framework with a focus on image alignment. Finally,…
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#014 Calculating Sparse Optical flow using Lucas Kanade method

Highlights: In this post, we will show how we can detect moving objects in a video using the Lucas Kanade method. This approach is based on tracking a set of distinctive feature points. Therefore, it is also known as a Sparse Optical Flow method. We will give a detailed theoretical understanding of the Lucas Kanade method and show how it can be implemented in Python using OpenCV. Tutorial overview: Understanding the Concept of Motion Optical…
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#015 Template matching using OpenCV in Python

Highlights: In this post, we’re going to talk about template matching which is probably the simplest form of object detection. Template matching is a technique in digital image processing that identifies the parts of an image that match a predefined template. It has various applications and is used in such fields as face and speech recognition, automation, and motion estimation. So, let’s begin with our post and see how template matching works in Python. Tutorial…
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#016 Feature Matching methods comparison in OpenCV

Highlights: Hi. Welcome to our new lecture. In our previous posts, we already explained in great detail how to extract distinct features (also called keypoints) from an image. Now we will learn how to compare two or more images by extracting pairs of identical feature points from those images. To accomplish this, we can apply several different feature matching methods that OpenCV provides. We hope that this post will complete your knowledge in this area…
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#013 Optical Flow Using Horn and Schunck Method

Highlights: A common problem in computer vision applications is the estimation of motion of every pixel in a sequence of images or video sequences. While many methods have been proposed to solve this problem, Optical Flow stands out as one of the most efficient ones. In this post, we will learn how to better understand a video sequence by analyzing how objects are moving across the frames using a popular Optical Flow estimation algorithm called…
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