# Vision-Based Traffic Sign Detection and Recognition Systems: Current Trends and Challenges
## 4 Traffic Sign Detection, Tracking and Classification Methods
### Classification Phase
The main disadvantage of SVM is lack of transparency of results. Transparency means how the results were obtained by the kernel and how the results should be interpreted. In SVM such things are unknown and cannot be known due to the high dimensional vector space. Examples of TSDR systems using a SVM method are shown in Table 12. \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** & **False** & **No. of** & & & \\  & & & **Positive** & **Positive** & **Test** & **Accuracy** & **Time** & **Dataset** \\ \hline
**[44]** & DtBs and SVM & Fast, high accuracy & N/A & N/A &
\begin{tabular}{c} Video \\ data \\ \end{tabular} & 92.3\% & N/A & GRAM \\ \hline
**[55]** & Gabor Filter & \
\begin{tabular}{c} Simple and high \\ accuracy \\ \end{tabular} & N/A & N/A & 58 & 93.1\% & N/A &
\begin{tabular}{c} Own \\ created \\ \end{tabular} \\ \hline
**[130]** & \begin{tabular}{c} CIELab and \\ Ramer–Douglas–Peucker \\ algorithm \\ \end{tabular} & \
\begin{tabular}{c} Illumination proof \\ and high accuracy \\ \end{tabular} & N/A & N/A & 405 & 97\% & N/A &
\begin{tabular}{c} Own \\ created \\ \end{tabular} \\ \hline
**[131]** & \
\begin{tabular}{c} RGB to HSI then shape \\ analysis \\ \end{tabular} & Less processing time & N/A & N/A & 92.6\% & Avg. 5.67 s &
\begin{tabular}{c} Own \\ created \\ \end{tabular} \\ \hline
**[88]** & \begin{tabular}{c} Hough transform \\ \end{tabular} & \begin{tabular}{c} Reliability \\ and accuracy \\ \end{tabular} & N/A & N/A & \
\begin{tabular}{c} Video \\ data \\ \end{tabular} & Avg. 92.3\% & 35 ms &
\begin{tabular}{c} Own \\ created \\ \end{tabular} \\ \hline
**[132]** & \begin{tabular}{c} RGB to HSI then shape \\ localization \\ \end{tabular} & \
\begin{tabular}{c} Reduce the memory \\ space and time for \\ testing new sample \\ \end{tabular} & N/A & N/A & N/A & 95\% & N/A &
\begin{tabular}{c} Own \\ created \\ \end{tabular} \\ \hline
**[133]** & \begin{tabular}{c} MSER \\ \end{tabular} & \
\begin{tabular}{c} Invariant in \\ illumination and \\ lighting condition \\ \end{tabular} & 0.97 & 0.85 & 43,509 & 89.2\% & N/A &
\begin{tabular}{c} Own \\ created \\ \end{tabular} \\ \hline
**[134]** & HSI and edge detection & Less processing time & N/A & N/A & \
\begin{tabular}{c} Video \\ data \\ \end{tabular} & N/A & N/A &
\begin{tabular}{c} Own \\ created \\ \end{tabular} \\ \hline
**[135]** & RGB to HSI & \
\begin{tabular}{c} Identify the optimal \\ image attributes \\ \end{tabular} & 0.867 & 0.12 & 650 & 86.7\% & 0.125 s &
\begin{tabular}{c} Own \\ created \\ \end{tabular} \\ \hline
**[136]** & \begin{tabular}{c} Edge Adaptive Gabor \\ Filtering \\ \end{tabular} & \
\begin{tabular}{c} Reliability and \\ Robustness \\ \end{tabular} & 85.93\% & 11.62\% & 387 & 95.8\%. & 3.5-5 ms &
\begin{tabular}{c} Own \\ created \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Examples of TSDR systems using a SVM method. In addition to these conventional methods, researchers have used other methods for recognition. In [138], the SIFT matching method was used for recognizing broken areas of a traffic sign. This method adjusts the traffic sign to a standard camera axis and then compares it with a reference image. [PERSON] et al. in [139] used principal component analysis (PCA) for both TSDR and the accuracy rate was approximately 99.2%. In [140], the researchers used improved fast radial symmetry (IFRS) for detection and a pictogram distribution histogram (PDH) for recognition. [PERSON] et al. in [141] used template matching followed by a three dimensional (3D) reconstruction algorithm to reconstruct the traffic signs obtained from video data and to improve the visual angle for detecting traffic signs. In [142], [PERSON] et al. used low rank matrix recovery (LRMR) to recover the correlation for classification with a hit rate of 97.51% in less than 0.2 s. [PERSON] et al. [143] used oriented gradient maps for feature extraction, which is invariant in illumination and variable lighting.