车辆尾灯灯语的检测与识别

Detection and Recognition the Signals of Vehicle Taillight

针对有效利用车辆灯语信息的问题,提出了一种基于图像的车辆尾灯灯语的检测识别新方法.该方法首先利用颜色、对称性特征在图像中检测出车辆尾灯对,并对车辆尾灯进行连续的跟踪.然后使用参数优化的最小二乘支持向量机(Least Squares Support Vector Machines,简称LS-SVM)对得到的车尾灯状态进行分类判别.最后结合状态历史信息,综合推断出各前方车辆当前灯语含义.以实车拍摄的白天道路视频进行实验,可以看到由识别出的灯语信息能够准确判断出前方车辆刹车、转向、灯不亮,表明该检测识别方法有效.

In this paper, a new algorithm of taillight signals detection and recognition based on image is proposed. Firstly, the algorithm detects and tracks vehicle taillights using color and symmetry features of the images. Secondly, we employ parameters-optimized Least Squares Support Vector Machines （LS-SVM） to classify the current states of detected taillights. Finally, we set up state chains from past frames to recognize the signals of taillights. Through the experiments on the real road, we can see that the recognized signals can correctly indicate the behaviors of brake and turn of preceding cars, which shows that our algorithm is valid.