Paper Title
Motion Detection Using Frame Difference, Background Subtraction, Optical Flow and Object Detection Techniques

Abstract
This study assesses the amalgamation of traditional image processing techniques with deep learning methodologies through a comparative analysis of motion detection methods. Frame difference, background subtraction (MOG, KNN), and optical flow (sparse and dense) techniques were evaluated under various scene conditions. A hybrid architecture was developed by combining the MOG approach with the YOLOv8 object detection model. Experimental assessments indicate that frame difference and background reduction techniques offer minimal computing expense but compromise accuracy in intricate backgrounds. The CUDA-enabled dense optical flow technique attained superior accuracies of roughly 0.78, 0.75, and 0.76 for Precision, Recall, and F1, respectively, although exhibited constrained performance regarding speed. The suggested MOG + YOLOv8 hybrid methodology attained the most equitable outcomes, with Precision values of 0.84, Recall values of 0.81, and F1 values nearing 0.82, ensuring great accuracy in the detection and categorization of moving objects. These findings indicate that the integration of classical techniques with deep learning models may serve as a potent option for future real-time monitoring and autonomous system applications. Keywords - Background Subtraction, Deep Learning, Hybrid System, Motion Detection, Optical Flow, YOLOv8