一种基于深度学习的行人检测分类方案设计

A Design of Pedestrian Detection and Classification Scheme Based on Deep Learning

行人检测分类是一种利用计算机视觉算法对图像或视频序列中的人物数量、年龄、性别以及衣着进行统计分类的技术。为了准确获取行人属性信息,降低行人漏检问题,经过分析现有数据集及行人所处的实际场景,提出基于深度学习的行人检测分类方案。首先使用行人检测算法检测行人身体属性,人脸识别算法识别人脸属性,然后结合身体属性与人脸属性对行人进行分类,最后总结了基于深度学习的行人检测分类以及发展前景。

Pedestrian detection classification is a technology that uses computer vision algorithms to statistically classify the number,age,gender,and clothing of people in an image or video sequence.In order to accurately obtain pedestrian attribute information and reduce the problem of pedestrian detection missing,a pedestrian detection classification scheme based on deep learning is proposed after analyzing the existing data set and the actual scenes of the pedestrian,First,the pedestrian detection algorithm is used to detect the body attributes of pedestrians and the face recognition algorithm is used to recognize the face attributes.Then the pedestrians are classified by according to the body attributes and face attributes.Finally,the classification and development prospects of pedestrian detection based on deep learning are summarized.