Gaussian Background Modeling for Object Detection
Gaussian background modeling demonstrates effective performance in object detection experiments through probabilistic scene modeling
Radar Digital Signal Processing Using MATLAB
Designing classical radar digital signal processing systems using MATLAB. This system processes radar target echoes, detects targets from noise, and extracts target range, velocity, and angle information. The tutorial is organized into five sections: 1) Radar LFM Signal Analysis, 2) Pulse Compression Processing, 3) Coherent Integration Processing, 4) Constant False Alarm Rate (CFAR) Processing, and 5) Target Information Extraction. Implementation includes MATLAB code for signal generation, filtering algorithms, and parameter optimization techniques.
High-Frequency Radar Target Detection Using Cell Averaging Constant False Alarm Rate Method
High-frequency radar target detection with cell averaging constant false alarm rate (CA-CFAR) method using simulated data, including algorithm implementation and performance analysis
Robot Vision Toolbox
Robot Vision Toolbox with extensive routine libraries, useful for video surveillance, object detection and tracking applications. Includes implementation of computer vision algorithms and pre-built processing pipelines.
Support Vector Machine for Image Classification and Segmentation
Support Vector Machine implementation for image classification, segmentation, object detection, and recognition in artificial intelligence information processing systems
High-Frequency Radar Target Detection with Maximum Likelihood Constant False Alarm Rate Method
High-Frequency Radar Target Detection Using Maximum Likelihood CFAR Approach with Weibull Distribution Modeling and Implementation Techniques
MATLAB Code Implementation for Multi-Target Tracking
MATLAB code for multi-target tracking featuring real-time updates through background subtraction method. The implementation demonstrates excellent performance when detecting and tracking a small number of targets (up to 8), but experiences significant performance degradation when tracking more than 8 targets.
Shadow Elimination Plays a Critical Role in Video Surveillance Systems
This code implements shadow removal for intelligent visual monitoring/video surveillance systems. Shadow elimination significantly enhances target detection by reducing false positives and missed detections, improving recognition efficiency, and increasing system stability and reliability. The implementation typically involves background modeling, color/texture analysis, and morphological operations to distinguish shadows from foreground objects.
MATLAB Implementation for Object Detection Using Contextual Relationships
Object Detection Utilizing Contextual Relationships - Algorithm Implementation and Code Examples
Target Detection in Sea Surface SAR Images Using Constant False Alarm Rate (CFAR) Algorithm
MATLAB implementation of CFAR algorithm for target detection in sea surface SAR imagery, including code structure and key parameter configurations