# Traffic Measurement and Congestion Detection Based on Real-Time Highway Video Data
## 4 Evaluation and Limitations
### Additional Limitations  
Moreover, the detailed counting results there are several more restrictions while analysing traffic on video sequences. Some examples about additional limitations are depicted in Figure 14. First of all, different weather conditions can have a great effect on the overall performance of the system. Fog, rain \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**Actual**} \\ \hline  & **Truck** & **Car** & **Motorbike** & **Lost** & **Sum** \\ \hline \multirow{4}{*}{Counted} & Truck & 9 & 0 & 0 & 0 & 9 \\ \cline{2-6}  & Car & 10 & 126 & 0 & 0 & 126 \\ \cline{2-6}  & Motorbike & 0 & 0 & 0 & 0 & 0 \\ \cline{2-6}  & Lost & 13 & 30 & 4 & 0 & 47 \\ \hline \multirow{2}{*}{} & Sum & 32 & 156 & 4 & 0 & 192 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Counting results per vehicle type from a highway with 1500 ms request interval. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**Actual**} \\ \hline  & & **Truck** & **Car** & **Motorbike** & **Lost** & **Sum** \\ \hline \multirow{4}{*}{Counted} & Truck & 22 & 0 & 0 & 0 & 22 \\ \cline{2-6}  & Car & 10 & 163 (+172) & 0 & 0 & 173 (+172) \\ \cline{2-6}  & Motorbike & 0 & 0 & 0 & 0 & 0 \\ \cline{2-6}  & Lost & 0 & 0 & 4 & 0 & 4 \\ \hline \multirow{2}{*}{} & Sum & 32 & 163 & 4 & 0 & 199 (+172) \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Counting results per vehicle type from a highway with 500 ms request interval. Nearly every vehicle is counted twice. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**Actual**} \\ \hline  & & **Truck** & **Car** & **Motorbike** & **Lost** & **Sum** \\ \hline \multirow{4}{*}{Counted} & Truck & 22 & 0 & 0 & 0 & 22 \\ \cline{2-6}  & Car & 10 & 158 (+36) & 0 & 0 & 168 (+36) \\ \cline{2-6}  & Motorbike & 0 & 0 & 0 & 0 & 0 \\ \cline{2-6}  & Sum & 32 & 158 & 4 & 0 & 194 (+36) \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Counting results per vehicle type from a highway with 1000 ms request interval. There are more vehicles counted then available. or snow can change the recorded scenario completely. On the one hand, the detection framework cannot properly detect vehicles if they are not entirely visible anymore due to occlusion. Another factor are sudden reflections on the road itself because the surface gets wet and vehicles have to drive with lights on. On the other hand, not the road conditions itself, but also the camera parameters can change. Rain or snow can pollute the lens which obviously leads to a restricted performance of the overall system. Additionally, as a result that the system analyses real-world scenarios, the day-night rhythm has to be considered as well. In some cases artificial light brightens the scene too much, thus vehicles or vehicle types are not distinguishable anymore. For scenes with complete darkness the detection of vehicles is nearly impossible. Most of the time only the front or back lights of the vehicle are identifiable, even for the human eyes. Furthermore, if vehicles are driving towards the camera the scene gets masked completely because of the high beam of vehicles. Another difficult scenario are multi-lane highways with exits or barriers. In principle, the prediction model needs constant traffic for all of the road parts in order to estimate the driving paths correctly. If the driving directions are separated and one of them is more used than the other tracking often leads to wrong results. This is a result of the wrong estimations of future positions from the prediction model. In some cases traffic scenes are recorded just before traffic lights. This often leads to rapid congestions on the one end of the scene whereas on the other end there is a normal traffic flow. Therefore, the prediction model cannot update its internal state accordingly which leads to difficulties in tracking of the congested vehicles. A special issue can be observed in town regions. There, traffic scenes do not show the roads itself, but also parked vehicles besides the road.