Abstract: Pedestrian detection is an important research field in computer vision.The key of pedestrian detection is feature selection.Most of pedestrian features are based on Papageorgiou’s rectangle features.In most circs，the pedestrian in video is small and faintness.The shortcoming of rectangle features is that they are not good in describing edge of pedestrian.In this paper，four new features called triangle features are presented and trained by an adaboost algorithm to get a representation of pedestrian.Unlike rectangle features，calculating triangle features is not easy. An algorithm to calculate triangle features is proposed in this paper.Compared with rectangle and asymmetry rectangle features，triangle features are good in describing edge of pedestrian.A set of experiments indicate that triangle features are better in describing local characteristics of people’s appearance and designing pedestrian detector.

摘要： 基于视频的行人检测技术是计算机视觉领域的重要问题，特征提取是行人检测技术的关键，大量的特征提取方法是基于Papageorgiou提出的矩形特征，但对于行人的边缘特性描述并不准确。针对adaboost算法，分析行人姿态的边缘特性，提出了四种较为有效的三角特征描述人体的外形局部特征，并推导了三角特征计算量与计算耗时的关系。与传统的矩形特征、非对称特征相比，三角特征能更有效的描述行人姿态，提高了行人检测的鲁棒性。