基于机器学习的车载单目远红外行人检测方法存在实时性较差和检测精度较低的问题 . 为此，文中提出了基于边缘分割的头部 - 方向梯度直方图 - 支持向量机（ Head-HOG-SVM ）行人检测方法，引入加权 Sobel 算子强化行人的垂直边缘以分割行人候选区域；根据不同距离行人的外观模式选择行人检测方法：使用头部特征检测中、近距离行人以改善系统的实时性，使用头部识别级联基于方向梯度直方图特征的支持向量机（ HOG-SVM ）分类器检测成像模糊的远距离行人 . 在多个郊区场景视频数据集上的实验结果表明，与基于双阈值分割的 HOG-SVM 分类方法相比，文中方法的检测精度和检测速度分别提高了约 33%和 200%.

As the pedestrian detection with vehicle-mounted far-infrared monocular sensor using machine learning is usually poor in real-time performance and precision ， a head-histogram of oriented gradient-support vector machine （ Head-HOG-SVM ） approach based on edge segmentation is proposed. The weighted Sobel operator is adopted to enhance the vertical edges of pedestrians in the regions of interest （ ROIs ） . Several pedestrian detec-tion methods are selected according to the pedestrian appearance in different distance. A head feature is used to detect pedestrians at near and middle distance to improve the real-time performance of the system ， and a HOG-SVM classifier cascading with head recognition is used to detect blurred pedestrians at far distance.Experimental results on the several videos captured from suburb scenes show that ， in comparison with the HOG-SVM classifier based on dual threshold segmentation ， the precision and detection rate of the proposed method are respectively increased by 33% and 200%.