- Volume 19 Issue 9
DOI QR Code
Performance Improvement of Traffic Signal Lights Recognition Based on Adaptive Morphological Analysis
적응적 형태학적 분석에 기초한 신호등 인식률 성능 개선
- Received : 2015.06.29
- Accepted : 2015.08.04
- Published : 2015.08.20
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.
Self-driving;Traffic Lights;Morphological Analysis;Distance Estimation
- R. Charette and F. Nashasibi, "Real Time Visual Traffic Lights Recognition Based on Spot Light Detection and Adaptive Traffic Lights Templates," Intelligent Vehicles Symposium, 2009 IEEE. IEEE, 2009.
- Y. Jie, C. Xiamin, G. Pengfei, and X. Zhonglong, "A New Traffic Light Detection and Recognition Algorithm for Electronic Travel Aid," 2013-4th International Conference on Intelligent Control and Information Processing (ICICIP), June 9-11, 2013.
- S. Kim, Y. Baek, and S. Moon, "Development of Traffic Light Automatic Discrimination System Using Digital Image Processing Technology", IEEK Signal Processing Society, Vol. 46, No.2, March 2009. pp.93-99
- Y. Kim, K. Lee, S. Cho, J. Park, and K. Choi, "Real-time Identification of Traffic Light and Road Sign for the Next Generation Video-Based Navigation System," journal of Korea Spatial Information System Society, Vol. 10, No. 2, June 2008, pp. 13-24
- M. Kim, J.H. Nam and J.W. Jang, "Implementation of Smart Car Infotainment System Including Black Box and Self-diagnosis Function," International Journal of Software Engineering and Its Applications, Vol.8, No.1, 2014, pp.267-274 https://doi.org/10.14257/ijseia.2014.8.1.23
- J. Jeong and D.Rho, "Real Time Detection and Recognition of Traffic Lights Using Component Subtraction and Detection Masks," IEEK Signal Processing Society, Vol. 43, No.2, March 2006. pp.65-72
- J. Ryu and J. Kim, "Road Distance Estimation Based on Pinhole Model for a Vehicle-attached Black Box Camera", IPIU2015, Jeju, Feb. 2015.
- J. Kim, "Effective Road Distance Estimation Using a Vehicle-attached Black Box Camera," Journal of Korea Institute of Info. and Comm. Eng., Vol.19, No.3, Mar 2015, pp.651-658. https://doi.org/10.6109/jkiice.2015.19.3.651
- M. Mathias, R. Timofte, R. Benenson, and L.V.Gool, "Traffic Sign Recognition - How Far are We from the Solution," Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2013.
- A. Lorsakul and J. Suthakorn, "Traffic Sign Recognition for Intelligent Vehicle/Driver Assistance System Using Neural Network on OpenCV", The 4th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI 2007), 2007. p. 22-24.