MATLAB Simulation for Ballistic Analysis
A MATLAB simulation program for ballistic analysis that accurately describes trajectory curves of fast-moving objects such as missiles, featuring customizable parameters and environmental factor modeling.
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
Motion Detection Implementation in MATLAB
A MATLAB-based motion detection program capable of identifying moving objects in video streams, implementing computer vision algorithms for real-time analysis and object tracking.
Target Tracking Using Block Matching Algorithm
Implement target tracking with block matching algorithm to display movement direction of moving objects and generate motion vector plots
Implementation Methods of OOSM in Target Tracking
Implementation methods of OOSM (Out-of-Sequence Measurements) in target tracking, covering: parameter information of moving objects and sensors, motion trajectory of target objects, movement trajectory of sensors, and measurement report vectors with code implementation insights
Real-Time Motion Tracking and Counting via USB Webcam Image Acquisition
Implementation of real-time image acquisition using USB webcam, featuring motion object tracking and counting through median filtering for background extraction with dynamic updates. The video processing algorithm combines frame differencing and background subtraction techniques, delivering effective performance though limited in resolving object occlusion scenarios.
Robust Color Feature Point Tracking for Moving Objects
This MATLAB-based program tracks distinctive color feature points on moving objects through advanced image processing algorithms. It performs real-time visual computations on captured image data to extract critical motion state information including velocity vectors and positional coordinates. The implementation utilizes color segmentation techniques and optical flow analysis for precise feature tracking. Extensively tested across diverse environments, this solution demonstrates practical utility with rapid processing capabilities, serving as a powerful localization tool for moving objects in experimental systems.
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.
Particle Filter Algorithm for Motion Tracking
Particle filter implementation for tracking moving objects, featuring probabilistic state estimation using sequential Monte Carlo methods.