机器视觉中的人体检测算法优化

Human Detection Algorithm Optimization in Machine Vision

提出了一种基于深度学习和景深信息的人体检测方法。采用基于深度学习的方法进行目标检测,运用深度图的景深信息判定人体的位置,将两者结合准确定位人体。本文方法通过深度摄像头采集RGB图及对应深度图,采用darknet-yolo-v3对RGB图进行目标检测,将目标边界框预处理后传给RGB图对应深度图,深度图采用无边界主动轮廓模型对景深信息进行处理,达到将深度学习的高识别率与景深信息结合精准定位人体目标的目的。实验结果表明,本文方法能准确找到一个不受标识框影响的目标定位点,有效改善由人体的不同姿态、动作幅度大小导致标识框误差增大的问题,提升了检测人体的精度,为进一步研究行人的准确跟踪提供了保障。

This paper proposes a human body detection method based on deep learning and depth of field information.The deep learning method is used for target detection and the depth of field information of the depth map is used to determine the position of the human body,and then the two works are combined to accurately locate the human body.In this method,the RGB image and the corresponding depth map are acquired by the depth camera,and the RGB image is detected by the darknet-yolo-v3.The obtained target bounding box is preprocessed and transmitted to the corresponding depth map of the RGB image,which processes the depth of field information adopting the active contour without edges model and get the aim of combing deep learning with high rate of recognition and depth of field information to accurately locate the target.The experimental results show that this method can accurately find a target positioning point that is not affected by the logo box,effectively improve the problem of increasing the mark box error caused by the different attitude and action amplitude of the human body,improve the accuracy of the detection of human body,and provide a guarantee for further study of pedestrian accurate tracking.