Recent developments in the field of emerging technologies including Artificial Intelligence and Machine Learning have led to wide interest in designing and developing innovative solutions in the area of traffic management and human safety. Multiple researchers have used these technologies to propose solutions for traffic sign image management that can lead to driver safety and reduction in number of accidents [10, 12, 14]. To prevent road accidents, traffic signages on roads are vital parameters that can help drivers to take timely decisions and preventive measures to avoid accidents. There is a strong need to improve the traffic sign identification and detection so that irrespective of weather conditions and degradation of sign boards, still driver navigation system is able to identify the correct signs and help in decision making ([13]; Bhatt and Tiwari, Smart traffic sign boards (STSB) for smart cities [Bhatt DP, Tiwari M (2019) Smart traffic sign boards (STSB) for smart cities. In: 2nd Smart Cities Symposium (SCS 2019), pp 1–4, March. IET]). Researchers have been using public traffic sign datasets to find ways to enhance the precision of image recognition approaches. In the previous published research, Vashisht and Kumar [21], have proposed a 3D color texture-based approach for detecting the traffic sign images by making use of ML algorithms and ANN. In this paper, the research is further extended using dimensionality reduction techniques used for feature reduction on Mapillary traffic sign image dataset. In terms of organizing the rest of the paper, the next section of background covers the previous research work done in dimensionality reduction. Next, the authors have explained the proposed methodology for feature selection and ANN design. Then, results from the implementation using ranking algorithms, classifier algorithms, and their comparisons are described. In the end, authors concluded the paper with suggested future direction of work.
Improved Traffic Sign Recognition System for Driver Safety Using Dimensionality Reduction Techniques
Lect. Notes in Networks, Syst.
International Conference on Micro-Electronics and Telecommunication Engineering ; 2023 ; Ghaziabad, India September 22, 2023 - September 23, 2023
22.03.2024
11 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
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