# Recognition of Traffic Sign Based on Bag-of-Words and Artificial Neural Network
## 2 Related Work  
Various traffic sign detection and recognition methods and algorithms have been developed. In recent years, several studies have proposed the intelligent traffic sign classification systems to classify ideogram-based traffic signs in real-time [16; 26; 27; 28; 29; 30]. [PERSON] et al. [27] developed a traffic sign classification system that utilized color information and Bags of Features (BoF) using SVM classifier to classify traffic signs. Their proposed system failed to recognize traffic signs in two instances, when the traffic sign is intensively illuminated and when the traffic sign has partially been occluded with the same color object. Some researchers carried out the investigation to just detect traffic signs without the use of classification [31; 32], whereby some researchers were focused on detection and recognition of the traffic signs [33]. Round road sign detection on Chinese highways was proposed by [PERSON] et al. [31]. The limitation of [31] is it can only apply to the detection and recognition of round shaped road signs and cannot identify any other shaped traffic sign. [PERSON] et al. [15] proposed a method which had three main phases: the first phase was image pre-processing, the second phase was detection and the last phase was recognition. In the detection phase, they used color segmentation with shape matching. Finally, SVM classifier was used to perform recognition and it achieved 95.71% system accuracy. [PERSON] et al. [34] proposed a sign recognition method for intelligent vehicles with smart mobile phones. Color detection was employed to perform in hue, saturation, and value (HSV) color space segmentation. Shape recognition based on template matching was done by using a similarity calculation. The optical character recognition (OCR) was utilized on the pixels inside the shape border to decide provided a match to the authentic sign. However, their proposed system was limited to only red color traffic signs. Moreover, very limited types of signs were used for classification. [PERSON] et al. [35] proposed a method of bag-of-visual-words technique with Speeded Up Robust Features (SURF) descriptors and SVM classifier which was used to identify the traffic signs. Their experimental results obtained an accuracy of 95.20%. [PERSON] et al. [36] introduced a multi-class traffic sign recognition system based on the BoW features model and extended it further by using a spatial histogram that incorporates rough layout of images and greatly improves the classification. Their proposed recognition system is performed by SVM. A two-stage fuzzy inference model was introduced by [PERSON] et al. [37] to detect the road traffic signs from a video frame and SVM was employed for recognition of road traffic signs. However, their proposed method was limited to only prohibitory and warning traffic signs. [PERSON] et al. [24] introduced a different technique for detection and recognition of traffic signs in real-time. They used Hough transformation to identify the ROI point to the traffic sign. Rotation invariant binary pattern (RIBP) was used to extract features and ANN was used for traffic signs classification. [PERSON] et al. [38] proposed a low-multifaceted speed limit detection and recognition process which not only supported distinctive sorts of speed breaking point signs from different nations but also maintained a great identification rate under severe climates. However, their proposed system was only limited to the speed limit traffic signs. As a subject of dynamic scholarly research, traffic sign recognition is additionally a technology that is being researched and enforced within the business industries. Many vehicle manufacturers(such as Tesla, Inc., Palo Alto, CA, USA; Continental AG, Hanover, Germany) develop the technology to detect and recognize the road traffic signs as a part of the smart vehicle. In 2010, the BMW-5 series (BMW, Munich, Germany) initiated the project of production of a traffic sign recognition system [39].