Lucas-Kanade Optical Flow Algorithm Implementation in MATLAB
MATLAB-based LK optical flow algorithm with Gaussian pyramid implementation, ready to run with included standard reference images for performance validation.
Motion Object Detection Using Optical Flow Method
A beginner-friendly program implementing optical flow for motion detection, featuring pixel intensity tracking and movement vector calculation.
Horn-Schunck Optical Flow Algorithm
Application Background The principle of optical flow method for detecting moving objects: assigning a velocity vector (optical flow) to each pixel in an image forms an optical flow field. If there are no moving objects, the optical flow field remains continuous and uniform. When moving objects are present, their optical flow differs from the background, causing discontinuities in the flow field, enabling detection of moving objects and their positions. Key Technologies 1.2 Horn-Schunck Model In 1981, Horn and Schunck introduced a variational approach by incorporating a global smoothness constraint based on the continuous and smooth characteristics of optical flow fields for moving objects. The energy equation consists of: - Data term representing brightness constancy constraint - Smoothness term enforcing flow field continuity
Video Tracking Using Optical Flow Method
Optical flow method for video tracking with MATLAB simulation implementation
A Classic Motion Image Object Detection System Using Optical Flow Method
A successfully debugged classical optical flow-based motion image object detection system with enhanced algorithm implementation details
Video Key Frame Extraction Using Optical Flow Method
This MATLAB source code implements optical flow-based key frame extraction from videos, serving as the reference implementation for our content-based video retrieval research paper. The implementation includes optical flow calculation, motion analysis, and similarity-based key frame selection algorithms.
Key Frame Extraction Code Implementation
My graduation project featuring MATLAB-based key frame extraction code, enhanced with optical flow method references. The implementation utilizes Euclidean distance, mean, variance, and coefficient of variation metrics for frame difference analysis. The thoroughly debugged code produces optimal results and is shared here for community benefit.
Optical Flow Method for Target Tracking and Segmentation
Implementation of target tracking and segmentation using optical flow method, designed for high-precision simulation and modeling applications
Fundamental Principles of Motion Detection Using Optical Flow Method
The core principle of optical flow-based motion detection involves assigning a velocity vector to each pixel in an image, forming an image motion field where pixels correspond to points on 3D objects through projection relationships at specific moments, enabling dynamic analysis through velocity vector characteristics. Implementation typically involves calculating displacement between consecutive frames using algorithms like Lucas-Kanade or Horn-Schunck.
Horn-Schunck Optical Flow Method
Implementation and Algorithm Analysis of Horn-Schunck Optical Flow Method