Estimation of Vehicular Velocity based on Non-Intrusive stereo camera
This work addresses vehicle velocity estimation for autonomous driving applications, but it is incremental as it combines existing methods.
The paper tackles the problem of estimating a leading vehicle's velocity using a non-intrusive stereo camera, achieving an RMSE of 0.416, which outperforms the baseline RMSE of 0.582.
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