The paper presents a modular approach for the estimation of a leading vehicle's velocity based on a non-intrusive stereo camera where SiamMask is used for leading vehicle tracking, Kernel Density estimate (KDE) is used to smooth the distance prediction from a disparity map, and LightGBM is used for leading vehicle velocity estimation. Our approach yields an RMSE of 0.416 which outperforms the baseline RMSE of 0.582 for the SUBARU Image Recognition Challenge

翻译：本文提出了一种基于非侵入式立体摄像头的前车速度估计模块化方法。采用 SiamMask 进行前车跟踪，采用核密度估计（KDE）对视差图距离预测进行平滑处理，采用 LightGBM 进行前车速度估计。我们的方法的 RMSE 为 0.416，优于 SUBARU 图像识别挑战的基线 RMSE 0.582。