# Vision-Based Traffic Sign Detection and Recognition Systems: Current Trends and Challenges
## 4 Traffic Sign Detection, Tracking and Classification Methods
### Classification Phase  
After the localization of ROIs, classification techniques are employed to determine the content of the detected traffic signs [1]. Understanding the traffic rule enforced by the sign is achieved by reading the inner part of the detected traffic sign using a classifier method. Classification algorithms are neither color-based nor shape-based. The classifier usually takes a certain set of features as the input, which distinguishes the candidates from each other. Different algorithms are used to classify the traffic signs swiftly and accurately. Some conventional methods used for classification of traffic signs are outlined in Figure 12 and reviewed respectively in Tables 6-13. Figure 11: An example of a TSDR system includes tracking process based on Kalman filter [81]. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Technique** & **Paper** & **Advantages** & **Performance** \\ \hline \multirow{8}{*}{**Kalman Filter**} & \multirow{8}{*}{[82]} & For avoiding incorrect assignment, & \multirow{2}{*}{N/A} \\  & & rule-based approach utilizing combined & \\  & & distance direction difference is used. & \\ \cline{2-3}  & & & \\ \cline{2-3}  & & & \\ \cline{2-3}  & & & \\ \cline{2-3}  & & & \\ \cline{2-3}  & & & \\ \hline \multirow{4}{*}{**Advanced Kalman Filter**} & \multirow{4}{*}{[85]} & Fast and advanced method, high detection & \multirow{2}{*}{Using \(400\times 300\) pixel images, \(\text{can process 3.26 frames}\)} \\  & & and tracking rate & & per second. \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Sign tracking based on Kalman Filter approaches. Template matching is a common method in image processing and pattern recognition. It is a low-level approach which uses pre-defined templates to search the whole image pixel by pixel or to perform the small window matching [15]. It was used for TSDR by [PERSON] et al. [90] and [PERSON] et al. [91]. It has the advantages of being fast, straightforward and accurate (with a hit rate of approximately 90% on their own pictured images dataset). However, the drawback of this method is that it is very sensitive to noise and occlusions. In addition, it requires a separate template for each scale and orientation. Examples of TSDR systems using a template matching method are shown in Table 6.  
Another common classification method is the random forest. It is a machine learning method that operates by constructing a multitude of decision trees during the training time and outputting the class that is the mode of the output of the class of individual trees. This method was compared in [92; 93] with SVM, MLP, Histogram of Oriented Gradient (HOG)-based classifiers, showing the highest accuracy rate and the lowest computational time. Based on their own dataset, the accuracy was approximately 94.2%, whereas the accuracy of the SVM is 87.8% and that of MLP is 89.2%. In terms of computational time for a single classification, the SVM takes 115.87 ms, MLP takes 1.45 ms, and a decision tree takes 0.15 ms. Despite its high accuracy and low computation time, the main limitation of a random forest is that a large number of trees can make the algorithm slow and ineffective for real-time predictions. Examples of TSDR systems using a decision tree method are shown in Table 7.  
Genetic algorithm is another classification method. It is based on a natural selection process that mimics biological evolution, which was used early in this century. This method was used for