Project Overview
This project focuses on detecting moving objects in video frames by subtracting the background. It applies machine learning and image processing techniques to enhance accuracy and efficiency in real-time object detection. By implementing methods such as Mask R-CNN and frame differencing, the model is optimized for real-time performance, making it useful for applications like surveillance, traffic monitoring, and motion analysis.
Key Features: Uses advanced background subtraction techniques to isolate moving objects. Incorporates Mask R-CNN for improved accuracy in object detection. Optimized for real-time video processing, ensuring low-latency detection. Potential applications include security surveillance, autonomous systems, and sports analytics.