Traffic light recognition is of great significance for driver assistance or autonomous driving. In this paper, a traffic light recognition system based on smartphone platforms is proposed. First, an ellipsoid geometry threshold model in Hue Saturation Lightness color space is built to extract interesting color regions. These regions are further screened with a postprocessing step to obtain candidate regions that satisfy both color and brightness conditions. Second, a new kernel function is proposed to effectively combine two heterogeneous features, histograms of oriented gradients and local binary pattern, which is used to describe the candidate regions of traffic light. A kernel extreme learning machine (K-ELM) is designed to validate these candidate regions and simultaneously recognize the phase and type of traffic lights. Furthermore, a spatial-temporal analysis framework based on a finite-state machine is introduced to enhance the reliability of the recognition of the phase and type of traffic light. Finally, a prototype of the proposed system is implemented on a Samsung Note 3 smartphone. To achieve a real-time computational performance of the proposed K-ELM, a CPU-GPU fusion-based approach is adopted to accelerate the execution. The experimental results on different road environments show that the proposed system can recognize traffic lights accurately and rapidly.
['Wei Liu', 'Shuang Li', 'Jin Lv', 'Bing Yu', 'Ting Zhou', 'Huai Yuan', 'Hong Zhao']
Real-Time Traffic Light Recognition Based on Smartphone Platforms