# Vision-Based Traffic Sign Detection and Recognition Systems: Current Trends and Challenges
## 4 Traffic Sign Detection, Tracking and Classification Methods
### Detection Phase
#### 4.1.2 Shape-Based Methods
The main advantage of the Hough transformation technique is that it is tolerant of gaps in feature boundary descriptions and is relatively unaffected by image noise [60]. However, its main disadvantage is the dependency on input data. In addition, it is only efficient for a high number of votes that fall in the correct bin. When the parameters are large, the average number of votes cast for a single bin becomes low, and thus, the detection rate is decreased. Another shape-based detection method is the similarity detection. In this method the detection is performed by computing a similarity factor between a segmented region and set of binary image samples representing each road sign shape [57]. This method was used by [PERSON] et al. [52] on their collected dataset with an accuracy level over 86.3%. The main advantage of this method is its straightforwardness, whilst its main drawback is that the input image should be perfectly segmented and the dimensions have to be same. In [52], the images were initially converted from RGB to HSV, then they were segmented and resized into \(36\times 36\) pixels. The similarity detection equation is,  
\[x^{\prime}=\frac{x-x_{\min}}{x_{\max}-x_{\min}}.ny^{\prime}=\frac{y-y_{\min}} {y_{\max}-y_{\min}}.n \tag{5}\]  
where, \(x_{\max}\), \(y_{\max}\), \(x_{\min}\) and \(y_{\min}\) are the coordinates of the rectangle vertices. Distance transform matching (DTM) is also another type of shape-based detection method. In this method, the distance transform of the image is formed by assigning each non-edge pixel a value that is a measure of distance to the nearest edge pixel. It was used by [PERSON] [61] to capture large variations in object shape by identifying the template features to the nearest feature image from a distribution of distances. This distance is inversely proportional to the matching of the image and the templates of the images. The chamfer distance equation is:  
\[D_{chamfer}(T,I)\equiv\frac{1}{T}\sum_{t\in T}d_{l}(t) \tag{6}\]  
where \(|T|\) and \(d_{l}(t)\) denote the number of features and the distance between features \(t\) in \(T\) and the closest feature in \(I\), respectively. In his experiment, [PERSON] [61] used DTM to examine 1000 collected test images, and the accuracy was approximately 95%. The DTM technique is efficient for detecting arbitrary shapes within images. However, its main disadvantage is the vulnerability of detecting cluttered images. Another popular two colorless traffic sign detection methods are edge detection features and Haar-like features. Edge detection refers to the process of identifying and locating sharp discontinuities  
Figure 10: Most popular shape-based detection methods. in an image [62]. By using this method, image data is simplified for the purpose of minimizing the amount of data to be processed. This method was used in [63; 64; 65; 66; 67] for indicating the boundaries of objects within the image through finding a set of connected curves. The Haar-like features method was proposed by [PERSON] and [PERSON] [68] based on the Haar wavelet to recognize the target objects. As indicated in Table 4, the Haar-like features based detection method was used in [69; 70] for traffic sign detection. The main advantage is its calculating speed, where any size of images can be calculated in a constant time. However, its weakness is the requirement of a large number of training images and high false positive rates [23].