基于高斯混合模型和AdaBoost的夜间车辆检测

Night-time vehicle detection based on Gaussian mixture model and AdaBoost

针对夜间车辆检测精度相对不高的问题,提出通过构建车头灯对空间几何关系的高斯混合模型(GMM)和采用逆投影车辆样本的Ada Boost分类器准确检测夜间车辆的方法。首先,在交通场景中根据车头灯对的空间位置关系设置逆投影面,通过图像预处理粗定位车灯区域;其次,在逆投影图像下利用车头灯对的空间几何关系构建车灯对的高斯混合模型,初步匹配车头灯对;最后,采用逆投影车辆样本,利用Ada Boost分类器进一步准确检测车辆。实验在3个交通场景的检测结果表明,与原始图像下的Ada Boost方法相比,所提方法的检测率提高了1.93%,漏检率降低了17.83%,误检率降低了27.61%;与D-S(Dempster-Shafer)证据理论方法相比,检测率提高了2.03%,漏检率降低了7.58%,误检率降低了47.51%。所提方法提高了相对检测精度,减少了地面反光和影子等的干扰,满足交通场景中夜间车辆检测的可靠性和准确性的要求。

Focusing on the issue that the accuracy of night-time vehicle detection is relatively low, a method of accurately detecting the night-time vehicles by constructing a Gaussian Mixture Model （GMM） for the geometric relationship of the headlights and an AdaBoost （Adaptive Boosting） classifier using inverse projected vehicle samples was proposed. Firstly, the inverse projection plane was set according to the spatial position relation of the headlights in the traffic scene, and the headlights area was roughly positioned by the image preprocessing. Secondly, the geometrical relationship of the headlights was used to construct the GMM with the inverse projected images, and the headlights were initially matched. Finally, the vehicles were detected by using the AdaBoost classifier for inverse projected vehicle samples. In the comparison experiments with the AdaBoost classifier for the original image, the proposed method increased detection rate by 1.93%, decreased omission ratio by 17.83%, decreased false detection rate by 27.61%. Compared with D-S （Dempster-Shafer） evidence theory method, the proposed method increased detection rate by 2.03%, decreased omission ratio by 7.58%, decreased false detection rate by 47.51%. The proposed method can effectively improve the relative detection accuracy, reduces the interference of ground reflection and shadow, and satisfies the requirements of reliability and accuracy of night-time vehicle detection in traffic scene.