基于时空关系模型的交通信号灯的实时检测与识别

交通信号灯的检测与识别是无人驾驶汽车和高级驾驶辅助系统(ADAS)的重要组成部分。针对城市道路复杂环境下的交通信号灯的检测和识别需求,依据多帧视频图像序列的时空连续变化关系构建多帧视频图像的时空关系模型(Time-Space Model,TSM),提出了一种新的基于多帧视频图像序列的交通信号灯的检测和识别算法。算法包含3部分:基于颜色的视频图像快速分割压缩算法,用于提高计算效率；引入多帧视频图像序列的时空关系模型,以提高交通信号灯检测的准确性 ；根据图像的HOG(Histogram of Oriented Gradient)特征,通过SVM(Support Vector Machine)分类器对信号灯进行识别。实验结果表明,算法的鲁棒性强、检测识别速度快、准确率高。

Real-time Detection and Recognition of Traffic Light Based on Time-Space Model

Detection and recognition of traffic light are important for driverless cars and advanced driver assistance systems(ADAS).In order to satisfy the requirements of traffic light detection and recognition in complex urban environment,a real-time detection and recognition algorithm based on time-space model (TSM) was proposed.It was established based on the time-space continuous variation relationship of video-frame sequence.The proposed algorithm consists of three parts.The first part is fast image segmentation and compression algorithm based on color,which is used to improve the computational efficiency.Second,time-space model of multi-frame image sequence is introduced to improve the accuracy of detection stage.Third,recognition of traffic lights is achieved by using support vector machine (SVM) with histogram of oriented gradients (HOG) features.Experiment results show that this novel algorithm has strong robustness,high efficiency and accuracy.