# Vision-Based Traffic Sign Detection and Recognition Systems: Current Trends and Challenges
## 4 Traffic Sign Detection, Tracking and Classification Methods
### Classification Phase
In addition, the AdaBoost trained classifier cannot be dynamically adjusted with new coming samples unless retrained from the beginning, which is time consuming and demands storing all historical samples [128]. Examples of TSDR systems using an AdaBoost method are shown in Table 11.  
Support vector machine (SVM) is another classification method that contracts an N-dimensional hyper plane that optimally separates the data into two categories. More precisely, SVM is a binary classifier that separates two different classes by a subset of data samples called support vectors. It was implemented as a classifier for traffic sign recognition in [129; 130; 131; 132; 133; 44; 134; 135; 136]. This classification method is robust, highly accurate and extremely fast which is a good choice for large amounts of training data. In [129], a SVM-based classifier was applied for detecting speed limit signs and it was compared with the artificial neural network multilayer perceptron (MLP), k-nearest neighbors (kNN), least mean squares (LMS), least squares (LS) and extreme learning machine (ELM) based classifiers. Results of the comparison demonstrated that the SVM-based classifier obtained the highest accuracy and lowest standard deviation amongst all other classifiers. Similarly, in a recent study [3], a cascaded linear SVM classifier was used for detecting speed limit signs, and the result was a recall of 99.81% with a precision of 99.08% on the GTSRB dataset. In [55], a SVM-based classifier was used to detect \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Ref** & **Detection Feature** & **Advantages** & \begin{tabular}{c} **True Positive** \\ **Rate** \\ \end{tabular} & \begin{tabular}{c} **False** \\ **Positive** \\ **Rate** \\ \end{tabular} & \
\begin{tabular}{c} **No. of** \\ **Test** \\ **Images** \\ \end{tabular} &
\begin{tabular}{c} **Overall** \\ **Accuracy** \\ \end{tabular} & **Time** & **Dataset** \\ \hline
[118] & \
\begin{tabular}{c} Object bounding \\ box prediction \\ \end{tabular} &
\begin{tabular}{c} Predicting position and \\ precise boundary \\ simultaneously \\ \end{tabular} & \textgreater{}0.88 mPA & \textgreater{}0.88 mPA & \textgreater{}3 pixels & 3.719 & 91.95\% & N/A & GTSDB \\ \hline
[120] & YCBCr model & \begin{tabular}{c} High accuracy \\ and speed \\ \end{tabular} & N/A & N/A & \
\begin{tabular}{c} Video \\ data \\ \end{tabular} & 98.6\% & N/A &
\begin{tabular}{c} Own \\ created \\ \end{tabular} \\ \hline
[111] & \
\begin{tabular}{c} Color space \\ thresholding \\ \end{tabular} &
\begin{tabular}{c} Implementing detection \\ and classification \\ \end{tabular} & 90.2\% & 2.4\% & 20,000 & 95\% & N/A & GTSRB \\ \hline
[121] & SVM & \begin{tabular}{c} Robust against \\ illumination changes \\ \end{tabular} & N/A & N/A & \
\begin{tabular}{c} Video \\ data \\ \end{tabular} & 97.9\% & N/A &
\begin{tabular}{c} Own \\ created \\ \end{tabular} \\ \hline
[117] & \
\begin{tabular}{c} Scanning window \\ with a Haar \\ cascade detector \\ \end{tabular} &
\begin{tabular}{c} Enhanced detection \\ capability with good \\ time performance \\ \end{tabular} & N/A & N/A & 16,630 & 99.36\% & N/A & GTSRB \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Examples of TSDR systems using a deep learning method. and classify red road signs in 1000 test images, and the accuracy rate was over 95%. In [88; 131], SVM was used with Gaussian kernels for the recognition of traffic signs, and the success rate was 92.3% and 92.6%, respectively. In [136], an advanced SVM method was proposed and tested with binary pictogram and gray scale images; the result was achieving high accuracy rates of approximately 99.2% and 95.9%, respectively. SVM has also shown great effectiveness in extracting the most relevant shots of an event of interest in a video, where a new SVM-based classifier called nearly-isotonic SVM classifier (NI-SVM) was proposed in [137] for prioritizing the video shots using a novel notion of semantic saliency. The proposed classifier exhibited higher discriminative power in event analysis tasks.