Explore MATLAB source code curated for "目标检测" with clean implementations, documentation, and examples.
Scale Invariant Feature Transform (SIFT) demonstrates exceptional performance in general object detection/recognition, with recent extensions to facial recognition applications. This MATLAB implementation includes feature extraction and descriptor computation capabilities.
A successfully debugged classical optical flow-based motion image object detection system with enhanced algorithm implementation details
High Frequency Radar, Target Detection, Constant False Alarm Rate Algorithm, Two-Parameter Ordered Statistics OSTWO
Application Context Fractal features characterize the roughness of object surface textures, with fractal dimension serving as their mathematical representation. Since man-made targets typically exhibit smooth surfaces while natural backgrounds tend to have rough textures, leveraging their dimensional differences provides a distinctive approach for target detection. Core Technology This project implements the box-counting method to calculate fractal dimensions of image regions, utilizing inter-regional dimensional variations for target localization. To enhance computational efficiency, we developed an improved fast box-counting algorithm that optimizes the conventional method through algorithmic refinements and parallel processing techniques.
Target detection through template matching methodology, a highly practical image processing technique with robust pattern recognition capabilities.
SIFT Algorithm for Accurate Image Matching and Object Detection with Scale-Invariant Feature Detection
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.
Object detection using background reconstruction techniques, including video sequence detection demonstrations
A comprehensive MATLAB-based object detection and recognition program designed for beginners learning MATLAB programming, featuring complete functionality across various applications