在城市交通流量视频检测系统中，夜间车辆检测一直是个难题。传统的方法都是基于形态学算子，通过检测车头灯来检测车辆，这种方法运算量较大，而且受环境光线影响比较大，为此，提出了一种基于颜色和运动信息的夜间车辆检测方法。该方法首先利用颜色信息在图像中检测出车辆尾灯，并对车辆尾灯进行连续的跟踪；然后利用运动信息和先验知识对车辆尾灯进行匹配；最后统计出交通流量。实验结果表明，该算法可以准确的检测出夜间正常行驶的车辆，并且能够适应雨天等复杂天气条件。

In the video detection system of urban traffic flow, it is a hard work to detect vehicle at night. The traditional methods are based on vehicle headlights and headlight beams using mathematical morphology, they are sensitive to environment illumination with high computing cost. This paper presents a method based on color and motion information. Firstly, the algorithm detects and tracks vehicle taillights using color information on the images, then matches the detected vehicle taillights using motion information and priori knowledge, finally vehicle flow is computed. As shown in experiments, the algorithm can exactly detect moving vehicles at night and it is robust to rainy conditions.