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http://dx.doi.org/10.6109/jkiice.2015.19.9.2129

Performance Improvement of Traffic Signal Lights Recognition Based on Adaptive Morphological Analysis  

Kim, Jae-Gon (School of Electronics, Telecom. & Computer Engineering, Korea Aerospace University)
Kim, Jin-soo (Department of Information and Communication Engineering, Hanbat National University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.19, no.9, 2015 , pp. 2129-2137 More about this Journal
Abstract
Lots of research and development works have been actively focused on the self-driving vehicles, locally and globally. In order to implement the self-driving vehicles, lots of fundamental core technologies need to be successfully developed and, specially, it is noted that traffic lights detection and recognition system is an essential part of the computer vision technologies in the self-driving vehicles. Up to nowadays, most conventional algorithm for detecting and recognizing traffic lights are mainly based on the color signal analysis, but these approaches have limits on the performance improvements that can be achieved due to the color signal noises and environmental situations. In order to overcome the performance limits, this paper introduces the morphological analysis for the traffic lights recognition. That is, by considering the color component analysis and the shape analysis such as rectangles and circles simultaneously, the efficiency of the traffic lights recognitions can be greatly increased. Through several simulations, it is shown that the proposed method can highly improve the recognition rate as well as the mis-recognition rate.
Keywords
Self-driving; Traffic Lights; Morphological Analysis; Distance Estimation;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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