Abstract
The traffic sign detection and recognition system is an essential module of the driver warning and assistance system. During the last few years much research effort has been devoted to autonomous vehicle navigation using different algorithm. Proposed work includes a neural network based drivers assistance system for traffic sign detection. In this paper authors have implemented a high-speed color camera to enhance its performance in real time scanning. The proposed algorithm increases the efficiency of the system by 7 to 10% as compared to conventional algorithms. The system includes two main modules: detection module and recognition module. In the detection module, the thresholding is used to segment the image. The features of traffic signs are investigated and used to detect potential objects. In recognition module, we use complimenting and then ANDing techniques. The joint use of classification and validation networks can reduce the false positive rate. There liability demonstrated by the proposed method suggests that this system could be a part of an integrated driver warning and assistance system based on computer vision technology.
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Bajaj, P., Dalavi, A., Dubey, S., Mouza, M., Batra, S., Bhojwani, S. (2005). Soft Computing Based Real-Time Traffic Sign Recognition: A Design Approach. In: Khosla, R., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2005. Lecture Notes in Computer Science(), vol 3681. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11552413_152
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DOI: https://doi.org/10.1007/11552413_152
Publisher Name: Springer, Berlin, Heidelberg
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