# Vehicle Speed Estimation Based on 3D ConvNets and Non-Local Blocks
## 4 Experiments and Analysis
### 3D ConvNets and Non-Local Neural Networks
#### 4.3.4 Comparison of Different Methods  
Table 7 shows the comparative experimental results of our method and other methods. Among the other methods, OptCalib, OptCalibVP2, OptScale, OptScaleVP2 are presented in [23] and the scale is computed as the mean of scale values obtained from the distance measurements on the road.  
It can be seen from the table our method underperforms OptScale, OptCalib, and OptCalibVP2. The main reason is the different error measure calculation. There are two sources of error to the camera calibration-based methods: (1) the error from camera calibration; (2) the error from detection and tracking. The error calculation of camera calibration-based methods presented here only contains the first error source, i.e., the error from camera calibration, whereas does not take account of the second error source, i.e., the detection and tracking cause error.  
However, the detection and tracking caused errors are not negligible. Since the speed calculation by camera calibration-based methods heavily relies on the results from the detection and tracking, an error in detection or tracking can greatly distort the vehicle trajectory, which in turn will lead to significantly off speed or even make the speed calculation undoable. It is clear that the errors that  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Input**} & \multicolumn{2}{c}{**IR(2+1)D50**} & \multicolumn{2}{c}{**IR(2+1)D50-c**} & \multicolumn{2}{c}{**IR(2+1)D50-cNL**} & \multicolumn{2}{c}{**IR(2+1)D50-cNLms**} \\  & **MAE** & **MSE** & **MAE** & **MSE** & **MAE** & **MSE** & **MAE** & **MSE** \\ \hline Optical flow & 3.02 & 17.35 & 2.97 & 16.61 & 2.85 & 15.55 & 2.81 & 14.81 \\ cat & 3.01 & 17.24 & 2.95 & 16.30 & 2.85 & 15.30 & 2.73 & 14.62 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Influence of non-local blocks and multi-scale convolution.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{1}{c}{**Configurations**} & \multicolumn{1}{c}{**Conv\_Add**} & \multicolumn{1}{c}{**Non-Local**} & \multicolumn{1}{c}{**Multi-Scale Conv**} \\ \hline IR(2+1)D50 & \(\times\) & \(\times\) & \(\times\) \\ IR(2+1)D50-c & \(\surd\) & \(\times\) & \(\times\) \\ IR(2+1)D50-cNL & \(\surd\) & \(\surd\) & \(\times\) \\ IR(2+1)D50-cNLms & \(\surd\) & \(\surd\) & \(\surd\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Different configurations of models.  
arise in detection and tracking will be carried on and compounded in the subsequent calculations. Besides, detection and tracking are prone to errors. In practice, a variety of common suboptimal environments (illumination variation, motion blur, background clutter and overlapping, etc.) could cause errors in the detection and tracking phases, including detection missing, false detection, tracking loss, etc. Considering the prevalence and significance of the detection and tracking errors, it is believed that the error results of the calibration-based methods would be profoundly greater with the neglected detection and tracking errors included. In contrast, our methods do not utilize detection and tracking, which means there are no the detection and tracking errors. Therefore, if detection and tracking-included error is taken into consideration, the error measure of the calibration-based methods would be higher than that of our methods.  
Our deep learning-based method to the average vehicle speed estimation problem heavily relies on data. In other words, a large quantity of labeled data needs to be accessible for the method to be effective. With extensive high quality data in place, our method is effective regardless of the environmental and weather conditions. In other words, our method is a self-closed end-to-end framework, which does not need manual intervention and does not rely on any other external factors nor subprocesses such as detection and tracking. However, there is an appreciable gap between the error measure of our method and that of GPS and RADAR.