# Vision-Based Traffic Sign Detection and Recognition Systems: Current Trends and Challenges
## 4 Traffic Sign Detection, Tracking and Classification Methods
### Classification Phase
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Ref**} & \multirow{2}{*}{**Detection Feature**} & \multirow{2}{*}{**Advantages**} & **True Positive** & **False Positive** & **No. of Test** & **Overall Accuracy** & **Time** \\ \cline{6-8}  & & & Positive & **Rate** & **Tested** & **Accuracy** & **Accuracy** \\ \hline \multirow{2}{*}{[90]} & RGB to HSV then & \multirow{2}{*}{Fast and straight} & \multirow{2}{*}{N/A} & \multirow{2}{*}{N/A} & \multirow{2}{*}{N/A} & \multirow{2}{*}{N/A} & \multirow{2}{*}{\textless{}99\%} & N/A \\  & contrast stretching & & & & & & \\ \cline{6-8}  & & & & & & & \\ \cline{6-8}  & N/A & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Examples of TSDR systems using a template matching method. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Ref**} & \multirow{2}{*}{**Detection Feature**} & \multirow{2}{*}{**Advantages**} & **True Positive** & **False Positive** & **No. of Test** & **Overall Accuracy** & **Time** \\ \cline{6-8}  & & & Positive & **Rate** & **Images** & **Accuracy** & **Accuracy** \\ \hline \multirow{2}{*}{[94]} & \multirow{2}{*}{HOG based SVM} & Used GTSER and ETH 80 dataset & \multirow{2}{*}{90.9\%} & \multirow{2}{*}{N/A} & \multirow{2}{*}{12,569} & \multirow{2}{*}{90.46\%} & \multirow{2}{*}{17.9 ms} & \multirow{2}{*}{GTSRB and ETH 80} \\  & & and compared & & & & & \\ \cline{6-8}  & & Used Gaussian weighting in & & & & & \\ \cline{6-8}  & & HOG to improve performance & 90\% & N/A & 12,569 & 97.2\% & 17.9 ms & Own created \\  & & by 15\% & & & & & \\ \hline \multirow{2}{*}{[92]} & \multirow{2}{*}{MSER based HOG} & Eliminating hand labeled & \multirow{2}{*}{83.3\%} & \multirow{2}{*}{0.85} & \multirow{2}{*}{
\begin{tabular}{} \end{tabular} } & \multirow{2}{*}{87.72\%} & \multirow{2}{*}{N/A} & \multirow{2}{*}{Own created \\  & & database, robust to various & & & & & \\ \cline{6-8}  & lighting and illumination & & & & & & \\ \hline \hline \multirow{2}{*}{[97]} & HOG & Remove false alarm up to 94\% & N/A & N/A & 12,569 & 92.7\% & 17.9 ms & Own created \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Examples of TSDR systems using a decision tree method. Figure 12: Most popular classification methods. traffic sign recognition by [PERSON] et al. [98] and [PERSON] et al. [99]. It was proved in these studies that this method is effective in detection of the traffic sign even if the traffic sign has some shape loss or illumination problem. The disadvantage of the genetic algorithm is non-deterministic work time and non-guarantee finding of the best solution [57]. Examples of TSDR systems using a genetic algorithm method are shown in Table 8.  
The other most common method for classification is using an artificial neural network (ANN). This method has gained an increasing popularity in recent years due to the advancement in general-purpose computing on graphics processing units (GPGPU) technologies [2]. In addition, it is popular due to its robustness, greater adaptability to changes, flexibility and high accuracy rate [100]. Another key advantage of this method is its ability to recognize and classify objects at the same time, while maintaining high speed and accuracy [2]. ANN-based classifiers were used in [56; 99; 101; 102; 103; 104; 105; 106; 107; 108] for TSDR. In the experiment conducted in [56], the hit rate was 97.6%, and the computational time was 0.2 s. However, in [107] ANN-based methods were described to have some limitations, such as their slowness and the instability in the NN training due to too large a step. This method was compared with a template matching method in [108], concluding that NNs require a large number of training samples for real world applications. Examples of TSDR systems using an ANN method are shown Table 9.  
Another increasingly popular method in vision-based object recognition is the deep learning method.