# Vision-Based Traffic Sign Detection and Recognition Systems: Current Trends and Challenges
## 4 Traffic Sign Detection, Tracking and Classification Methods
### Detection Phase
#### 4.1.1 Color-Based Methods  
Color-based methods take advantage of the fact that traffic signs are designed to be easily distinguished from their surroundings, often colored in highly visible contrasting colors [17]. These colors are extracted to detect ROI within an input image based on different image-processing methods. Detection methods based on the color characteristics have low computing, good robustness and other characteristics, which can improve the detection performance to a certain extent [25]. However, methods based on color information can be used with a high-resolution dataset but not with grayscale images [23]. In addition, the main problem with using the color parameter is its sensitivity to various factors such as the distance of the target, weather conditions, time of the day, as well as reflection, age and condition of the signs [17; 23]. In color-based approaches, the captured images are partitioned into subsets of connected pixels that share similar color properties [26]. Then the traffic signs are extracted by color thresholding segmentation based on smart data processing. The choice of color space is important during the detection phase, hence, the captured images are usually transformed into a specific color space where the signs are more distinct [9]. According to [27], the developed color-based detection methods are based on the red, green, blue (RGB) color space [28; 29; 30], the hue, saturation, and value (HSV) color space [31; 32], the hue, saturation, and intensity (HSI) color space [33] and various other color spaces [34; 35]. The most common color-based detection methods are represented in Figure 9 and reviewed respectively in Table 3.  
Color thresholding segmentation is one of the earliest techniques used to segment digital images [26]. Generally, it is based on the assumption that adjacent pixels whose value (grey level, color value, texture, etc.) lies within a certain range belong to the same class [36]. Normal color segmentation was used for traffic sign detection by [PERSON] et al. [37] with their own created dataset,  
Figure 8: Different methods applied for traffic sign detection. Figure 9: Most popular color-based detection methods. containing 2000 test images, resulting in an accuracy level of 82%. The efficiency was improved in [38] by using color segmentation followed by a color enhancement method. In recent research, color thresholding has commonly been used for pre-processing purposes [39; 40]. In [39], pre-filtering was used to train a color classifier, which created a regression problem, whose core was to find a linear function, as shown in (1). \[f(x)=\left(w,x\right)+b,x=\left(v_{1},v_{2},v_{3}\right)^{i} \tag{1}\]  
where \(v_{i}\) is the intensity value of \(i_{th}\) channel (i = 1, 2, 3 for a three-channel RGB image), \(\left(w,b\right)\in\Re\times\Re\) are parameters that control the function and the decision rule is given by \(\text{sgn}(f(x))\). In [40], [PERSON] et al. used RGB to HSI color space conversion with the additional feature of white sign detection. The main advantage of this feature is its illuminated sign detection. In Refs. [33; 41; 42; 43; 44; 45], HSI/HSV transformation approach was used for the purpose of detection. The major advantages of the HSI color space over the RGB color space are that it has only two components, hue and saturation, both are very similar to human perception and it is more immune to lighting conditions. In [33], a simple RGB to HSI color space transformation is used for the TSDR purpose. In [44], the HSI color space was used for detection, and then, the detected signal was passed to the distance to borders (DtBs) feature for shape detection to increase the accuracy level. The average accuracy was approximately 88.4% on GRAM database. The main limitation of using HSV transformation is the strong hue dependency of brightness. Hue is only a measurement of the physical lightness of a color, not the perceived brightness. Thus, the value of a fully saturated yellow and blue is the same.