Abstract
Many traffic accidents occurred at intersections are caused by drivers who miss or ignore the traffic signals. In this paper, we present a method dealing with automatic detection of traffic lights that integrates both image processing and support vector machine techniques. Firstly, based on the color characteristics of traffic lights, the paper proposes a method of traffic light segmentation in RGB and HSV color space. And then, according to the geometric features and backplane color information of traffic lights, we design an algorithm to remove false targets in images. Moreover, in order to solve traffic lights diffusion problem, we apply a strategy that we first map the candidate regions onto the original image, then using Otsu algorithm re-extract the target region. Finally, HOG features are extracted from the target regions, and recognized by the trained SVM classifier. Experimental results show that the proposed method has relatively high detection rate and recognition accuracy in different natural scenarios, and is able to meet real-time requirements.
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Acknowledgment
This work was supported in part by the National Natural Science Foundation of China under Grant 61403060, and the Natural Science Foundation of the Jiangsu Higher Education Institutions under Grant 15KJA460003. The authors would like to thank the anonymous reviewers and the editors for their many helpful suggestions.
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Wang, W., Sun, S., Jiang, M. et al. Traffic lights detection and recognition based on multi-feature fusion. Multimed Tools Appl 76, 14829–14846 (2017). https://doi.org/10.1007/s11042-016-4051-5
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DOI: https://doi.org/10.1007/s11042-016-4051-5