# Real-Time Traffic Sign Detection and Recognition Method Based on Simplified Gabor Wavelets and CNNs
## 4 Traffic Sign Detection
### Traffic Sign Detection  
Through preliminary filtering of the MSERs by the filter rules, we were able to greatly reduce the number of proposed traffic sign areas. In other words, the number of candidate areas was reduced without reducing the recall rate. From the point of view of processing time, the decline in the number of the proposed regions meant saving time in the next stage. In addition, it avoided the multi-scale sliding window detection method, which is time consuming and has low efficiency. As a basic demand for traffic sign detection, a more detailed classification algorithm must be used to divide candidate regions into their super classes.  
To obtain the super classes that the proposed areas should belong to, we first extracted the Histogram of oriented gradient (HOG) features from the SGW feature map, which we named the SGW-HOG features, and then used a multiclass SVM classifier trained with SGW-HOG features. As there were two super classes in the traffic signs, we trained a three-class SVM as there was a third super class of negative samples.  
In the process of transforming the SGW map to a HOG feature map, we did not use the method of transforming the whole map to the HOG feature map, but only transformed the candidate proposed regions. In this way, processing time was greatly saved. The processing operation is shown in Figure 8.  
The HOG feature is characterized by the gradient direction information of the local region of the image as the representation of the local image region; the process of HOG feature extraction based on SGW features is as follows:  
* The normalized sample image is used as the input, and the gradients in the horizontal and vertical orientations are calculated by the gradient operator.
* Statistic local image gradient information, where the sample image is divided into several pixels (cell), and the gradient direction is divided into nine intervals (bins). In each unit, the gradient  
Figure 8: The processing operation for super class classification.  
Figure 7: **(a)** Original scene RGB image; **(b)** grayscale image; **(c)** synthetic SGW filtered map; **(d)** maximally stable extremal regions (MSERs) on grayscale map; **(e)** MSERs on synthetic SGW filtered map; and **(f)** segmentation results after taking account the filter rules.  
direction of all pixels is counted in the direction interval, and a nine-dimension eigenvector is obtained.
* The adjacent four units form a block, so the characteristic vectors of all the elements in a block are connected in series to obtain the feature description vectors of the block region (36 dimensions).
* The sample image is scanned in blocks, and the scanning step is the cell. Finally, all the features of the block are combined to get the feature of the sample.  
In the detection stage, we obtained information on the location and the size of the traffic signs in the traffic scene. Furthermore, we determined the super classes of traffic signs, to deduce whether the traffic sign was a circular or triangle one. After this work, we were able to classify the traffic signs of the two super classes into their subclasses independently.