  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
