Human Body Detection MATLAB Program

This article presents a MATLAB-based human body detection program specifically developed for processing the HumanEva dataset, which is widely adopted in human pose estimation research. Human pose estimation represents a critical research direction in computer vision, with applications spanning human-computer interaction, motion analysis, and game development. Our implementation incorporates several computer vision and machine learning techniques, including human keypoint detection, pose estimation algorithms, and deep learning architectures. The program utilizes MATLAB's Image Processing Toolbox for dataset preprocessing operations such as image denoising using Gaussian filters, automated cropping through bounding box detection, and image rotation transformations. For pose estimation, we implement a multi-stage convolutional neural network (CNN) approach that first detects human bounding boxes using a region proposal network (RPN), then applies articulated pose estimation through sequential limb detection. The code structure includes modular functions for data loading (loadHumanEvaData.m), preprocessing (preprocessFrames.m), and pose inference (estimatePose.m). Our technical implementation demonstrates how these integrated techniques enhance detection accuracy while providing valuable benchmarking data for related research domains.