Recent Developments in Traffic Signs Recognition Techniques
Priyanka Satish Tekadpande1, Ramnivas Giri2
1Priyanka Satish Tekadpande, is a student of Shri-Shankaracharya College of Engineering, Chhattisgarh Swami Vivekanand Technical University, Bhilai, Chhattisgarh.
2Ramnivas Giri, is a faculty of computer science and engineering department, Shri-Shankaracharya College of Engineering, Chhattisgarh Swami Vivekanand Technical University, Bhilai, Chhattisgarh.
Manuscript received on July 12, 2012. | Revised Manuscript received on June 22, 2012. | Manuscript published on June 30, 2012. | PP: 39-43 | Volume-1 Issue-5, June 2012. | Retrieval Number: E0406051512/2012©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The traffic signs play a vital role for accident free and smooth fast driving. If the recognition of traffic signs done by the accurate and fast automated systems, it provides the extra edge in efficient navigation. Thus automatic traffic signs recognition is an important task, particularly in intelligence transportation system. Automated recognition system collects useful information about traffic signs, helps the driver to make timely decisions, and increases driving safety and comfort. This paper presents an overview of the different methods and techniques used in traffic sign detection and recognition. It describes the physical properties and characteristics of the road signs, potential difficulties and problems that occur during detection of real-time images. The detection and recognition techniques are classified into three stages i.e. Color-based filtering, shape-based analysis and final recognition. Thus, we have chronologically discussed some of the referred previous work theme-wise with respect to the different approaches and techniques used in these stages. In future, new techniques should be involved to increase the robustness, and to get faster systems for real-time applications.
Keywords: Artificial intelligence, Driver assistance system, Thresholding, hough Transform.