Automatic Target Detection Using Background Subtraction with Meanshift Tracking

This program initially utilizes background subtraction for automatic target detection and subsequently employs meanshift tracking for target following. Background subtraction serves as a fundamental target detection approach that identifies target positions by comparing differences between current frame images and background reference images. When the target completely enters the image frame, the program transitions to meanshift tracking - a color histogram-based method that provides more precise target position tracking through kernel-based density estimation. The program implements this by first creating a background model through statistical analysis of initial frames, then applying thresholding to difference images for target segmentation. For meanshift implementation, the algorithm calculates the target's color histogram distribution and iteratively moves the tracking window toward the region of maximum probability density using gradient ascent. By combining these two methodologies, the program achieves accurate target detection and robust tracking performance, making it widely applicable in image processing and computer vision applications such as surveillance systems and motion analysis.