# Vision-Based Traffic Sign Detection and Recognition Systems: Current Trends and Challenges
## 4 Traffic Sign Detection, Tracking and Classification Methods
### Classification Phase
This method has acquired general interest in recent years owing to its high performance of classification and the power of representational learning from raw data [109; 110]. Deep learning is a part of a broader family of machine learning methods. In contrary to task specific methods, deep learning focuses on data representations with supervised, weakly supervised or unsupervised learning. Deep learning methods use a cascade of many layers of nonlinear processing units for feature extraction and transformation. Each successive layer uses the output from the previous layer as input. Higher level features are derived from lower level features to form a hierarchical representation [110]. Among the deep learning models, the convolutional neural networks (CNN) have acquired unique noteworthiness from their repeatedly confirmed superiorities [111]. According to [112], CNN models are the most widely used deep learning algorithms for traffic sign classification to date. Of the examples applied to traffic sign classification are committee CNN [113], multi-scale CNN [114], multi-column CNN [102], multi-task CNN [111; 115], hinge-loss CNN [116], deep CNN [46; 117], a CNN with diluted convolutions [118], a CNN with a generative adversarial network (GAN) [119], and a CNN with SVM [120]. Based on these studies, a simultaneous detection and classification can be achieved using deep learning-based methods. This simultaneousness results in improved performance, boosted training and testing speeds. Examples of TSDR systems using a deep learning method are shown in Table 10. \begin{table}
\begin{tabular}{c l l c c c c c c} \hline \hline \multirow{2}{*}{**Ref**} & \multirow{2}{*}{**Detection Feature**} & \multirow{2}{*}{**Advantages**} & **True** & **False** & **No. of** & **Overall** & \multirow{2}{*}{**Time**} & \multirow{2}{*}{**Dataset**} \\  & & & **Positive** & **Positive** & **Test** & & **Accuracy** & \\  & & & **Rate** & **Rate** & & & & \\ \hline \multirow{3}{*}{[56]} & YCbCr and & \multirow{3}{*}{Robustness and } & \multirow{3}{*}{0.96} & \multirow{3}{*}{0.08} & \multicolumn{3}{c}{\(640\times 480\)} \\  & normalized cross & & & & px video & & & \\  & correlation & adaptability & & & & & & \\ \hline \multirow{2}{*}{[101]} & N/A & Flexibility and & \multirow{2}{*}{N/A} & \multirow{2}{*}{N/A} & \multirow{2}{*}{N/A} & \multirow{2}{*}{98.52–99.46\%} & \multirow{2}{*}{N/A} & Own \\  & & high accuracy & & & & & & created \\ \hline \multirow{2}{*}{[106]} & Adaptive shape & \multirow{2}{*}{Invariant in } & \multirow{2}{*}{N/A} & \multirow{2}{*}{N/A} & \multirow{2}{*}{220} & \multirow{2}{*}{95.4\%} & \multirow{2}{*}{0.6 s} & Own \\  & analysis & & & & & & & created \\ \hline \multirow{2}{*}{[107]} & NN & Robustness & N/A & N/A & 467 & N/A & N/A & Own \\  & & & & & & & created \\ \hline \multirow{2}{*}{[108]} & Bimodal & \multirow{2}{*}{Compared TM} & \multirow{2}{*}{0.96} & \multirow{2}{*}{0.08} & \multicolumn{3}{c}{\(640\times 480\)} \\  & binarization and & and NN & & & px video & & & \\ \multicolumn{2}{c}{thresholding} & & elaborately & & & data & & \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Examples of TSDR systems using an ANN method. Adaptive boosting or AdaBoost is a combination of multiple learning algorithms that can be utilized for regression or classification [15]. It is a cascade algorithm, which was introduced by [PERSON] and [PERSON] [122]. Its working concept is based on constructing multiple weak classifiers and assembling them into a single strong classifier for the overall task. As indicated in Table 11, the AdaBoost method was used for TSDR in [123; 124; 125; 126; 127]. Based on these studies, it can be concluded that the main advantage of the AdaBoost is its simplicity, high prediction power and capability to cascade an architecture for improving the computational efficiency. However, its main disadvantage is that if the input data have wide variations or abrupt changes in the background, then the training time increases and classifier accuracy decreases [121].