# Recognition of Traffic Sign Based on Bag-of-Words and Artificial Neural Network
## 4 Experimental Results  
Our experimental results showed that ANN outperformed SVM and Ensemble subspace _k_NN. Moreover, there is a very marginal difference between the results obtained by SVM and Ensemble subspace _k_NN.  
The SVM classifier achieved 92.30% accuracy when it was tested on real-time images. The detailed confusion matrix and misclassification error rate of SVM classifier are shown in Figure 12a,b respectively. The SVM classifier obtained the maximum misclassification error rate of 16%, 14%, and 13% for the classes _'No Waiting'_, _'Keep Left Curve Chevron'_, and _'No Entry'_ respectively. Moreover, SVM obtained the lowest misclassification error rate of 1% for classes _'Stop'_ and _'Towing Zone'_.  
Figure 11: Artificial neural network (ANN) architecture.  
Figure 12: Support vector machine (SVM) classifier result; (**a**) Confusion matrix; (**b**) Misclassification error rate.  
The Ensemble subspace \(k\)NN classifier achieved 92.70% accuracy when it was tested on real-time images. The detailed confusion matrix and misclassification error rate of Ensemble subspace \(k\)NN classifier are shown in Figure 13a,b respectively. The Ensemble subspace \(k\)NN classifier obtained the maximum misclassification error rate of 25%, 16%, and 15% for the classes '_No Entry_', '_No Waiting_', and '_No Stopping_' respectively. Moreover, Ensemble subspace \(k\)NN classifier obtained no error for class '_Towing Zone_' and 1% error rate for classes '_Keep Right Curve Chevron_' and '_Speed Limit_'.  
The ANN classifier achieved 99.00% accuracy when it was tested on real-time images. The detailed confusion matrix and misclassification error rate of ANN classifier are shown in Figure 14a,b respectively. Figure 14a shows the correct classification results in green squares and the incorrect classification results in red squares. In addition, the lower right blue square illustrates the overall accuracy of ANN classifier. As shown in Figure 14b, the ANN classifier obtained the maximum misclassification error rate of 7% for class '_No Waiting_'. Moreover, it obtained 2% misclassification error rate for classes '_Keep Left Curve Chevron_', '_No Entry_', and '_Speed Limit_'. For all other remaining classes, the ANN showed 0% misclassification error rate.  
As shown in experimental results, ANN classifier outperformed SVM, and Ensemble subspace \(k\)NN classifier. Hence, to compare the performance of our proposed system with baselines, we have compared the results of the ANN classifier. The compare the performance of our proposed system,  
Figure 14: ANN classifier result; (**a**) Confusion matrix; (**b**) Misclassification error rate.  
Figure 13: Ensemble subspace k-nearest neighbors (\(k\)NN) classifier result; (**a**) Confusion matrix; (**b**) Misclassification error rate.  
we compare our results with eight baselines [1; 15; 17; 24; 35; 57; 58]. The evaluation results showed the outperformance of our proposed system when compared to eight baselines. The detailed results are shown in Table 2. Here, it can be seen that all these compared studies have used the traffic sign dataset to classify traffic signs using machine learning approaches. However, our proposed method of traffic sign detection and recognition is quite different from the existing studies. In our proposed system, we have employed the state-of-the-art BoW model for feature extraction. This model uses SURF for the identification of feature descriptors. Moreover, to determine the best interest points of a traffic sign, our proposed method uses the \(k\)-means clustering approach. Moreover, we have used the ANN, which is robust classifier as compared to other related classifiers. Figure 15 shows some real-time experimental results of our developed system using ANN classifier.