Fast Automatic Shoot Score Approach Combining Optical Flow And Mobilenetv2 Classification

Abstract

This paper presents a fast automatic shooting scoring system designed for fixed-camera setup in real shooting range, where the paper target may undergo slight physical movement caused by wind, recoil-induced stand vibration. The proposed method combines an initial fixed homography with continuously updated dense optical flow for drift-free global alignment, residual optical flow for sub-pixel frame-to-frame registration, adaptive background modeling via motion-compensated subtraction for bullet hole candidate generation, and a highly efficient MobileNetV2 classifier for robust false-positive rejection. By maintaining precise registration to a static high-resolution Score Image template and leveraging the lightweight yet powerful quantized MobileNetV2 network, the system achieves over 98.5\% scoring accuracy at more than 10 fps entirely on CPU even when the target exhibits light movement and under changing natural lighting. Extensive experiments on indoor and outdoor live-fire datasets confirm robustness and real-time performance compared to traditional per-frame feature-matching methods and heavyweight deep learning approaches.