Explore MATLAB source code curated for "跟踪" with clean implementations, documentation, and examples.
MATLAB Object Detection and Tracking with related literature, MATLAB source code, and video files. This resource package includes practical implementations featuring algorithms like background subtraction, optical flow, and Kalman filtering for robust object detection and tracking solutions.
Application Background: This project implements vehicle detection and tracking in video sequences, where detection results are marked with red bounding boxes and tracking results with green bounding boxes. The code includes a series of motion vehicle frame images. Key Technologies: Vehicle detection utilizes background subtraction method with average background updating, while tracking employs Kalman filtering. The implementation features detailed code explanations with detection and tracking results visually distinguished by colored bounding boxes.
MATLAB source code for GPS software receiver implementation, featuring signal acquisition and tracking algorithms, demodulation capabilities, parameter customization, and real-time result visualization for each processing stage.
Source code implementation for face detection and tracking in video images with algorithm explanations
MATLAB implementation for strapdown inertial navigation system transfer alignment with tracking and time delay compensation
MATLAB-based step-by-step implementation of Kalman filter tracking and filtering functionality with algorithmic explanations
DSST tracking algorithm implementation requires placing target sequence images in the sequences folder for video object tracking
This implementation utilizes the frame difference method for motion target detection to effectively detect and track pedestrians in video sequences, demonstrating excellent performance that can serve as a valuable reference for learning and implementation.
This program utilizes system objects to demonstrate vehicle detection and tracking techniques in video sequences through optical flow estimation, highlighting practical implementation with MATLAB's Computer Vision Toolbox functions.
Implementation of walking pedestrian detection and tracking in intelligent transportation systems using computer vision algorithms, featuring motion-based recognition and real-time monitoring capabilities.