# Vision-Based Traffic Sign Detection and Recognition Systems: Current Trends and Challenges
## 6 Conclusions and Suggestion  
The major objective of the paper was to analyze the main direction of the research in the field of automatic TSDR and to categorize the main approaches into particular sections to make the topics easy to understand and to visualize the overall research for future directions. Unlike most of the available review papers, the scope of this paper has been broadened to cover all recognition phases: Detection, tracking and classification. In addition, this paper has tried to discuss as many studies as possible, in an attempt to provide a comprehensive review of the various alternative methods available for traffic sign detection and recognition; including along with methods categorization, current trends and research challenges associated with TSDR systems. The overall summary is presented in Figure 14.  
The conducted review reveals that research in traffic sign detection and recognition has grown rapidly, where the number of papers published during the last three years was approximately 280 papers, which represents about 41.69% of the total number of papers published during the last decade as a whole. With regard to the methods used, it was observed that the subject of traffic sign detection and recognition incorporates three main steps: Detection, tracking and classification; and in each step, many methods and algorithms were applied, each has its own merits and demerits. In general, the methods applied in detection and recognition consider either color or shape information of the traffic sign. However, it is well known that the image quality in real-world traffic scenarios is usually poor; due to low resolution, weather condition, varying lighting, motion blur, occlusion, scale and rotation and so on. In addition, traffic signs are usually in a variety of appearances, with high inter-class similarity, and complicated backgrounds. Thus, proper integration of color and shape information in both detection and classification phases is a very promising and exciting task that is in need of much more attention. For tracking, the Kalman filter and its variations are the most common methods. For classification, artificial neural network and support vector machine-based methods were found to be the most popular methods, with a high detection rate, high flexibility and easy adoptability. Despite the recent improvements in the overall performance of TSDR systems, more research is still needed to achieve a rigorous, robust and reliable TSDR system. It is believed that TSDR system  
Figure 14: Summary of the paper. performance can be enhanced by merging the detection and classification tasks into one step rather than performing them separately. By doing so, classification can improve detection and vice versa. Another idea for further improvement of TSDR is by using standard, sufficient and large databases for learning, testing and evaluation of the proposed algorithms. In this way, the TSDR system will be able to recognize the eight different categories of the traffic signs in the real environment with different conditions. This paper will be a useful reference for researchers looking for an understanding of the current status of research in the field of TSDR and finding the related research problems in need of solutions. Conceptualization, M.A.H. and A.H., S.A.S. ; methodology, M.A.H., M.A.A. and S.B.W. ; software, M.A.A. ; validation, S.B.W. ; formal analysis, M.A.A. ; investigation, M.A.H., and M.B.M. ; resources, M.A.H., S.B.W. ; data curation, M.A.A. and S.B.W. ; writing--original draft preparation, M.A.H., M.A.A., S.B.W., P.J.K. ; writing--review and editing, M.A.H., P.J.K., A.H., S.A.S. and M.B.M. ; visualization, M.A.A. ; supervision, M.A.H., S.A.S. ; project administration, M.A.H. ; funding acquisition, M.A.H. This research was funded by the TNB Bold Strategic Grant 510050797 under the Universiti Tenaga Nasional and the Universiti Kebangsaan Malaysia under Grant DIP-2018-020. This work is supported by the collaboration between Universiti Tenaga Nasional and Universiti Kebangsaan Malaysia.