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Method for detecting pedestrians in image by using Gaussian penalty

机译:通过使用高斯罚款检测图像行人的方法

摘要

Disclosed is a method for detecting pedestrians in an image by using Gaussian penalty. Initial pedestrian boundary box is screened using a Gaussian penalty, to improve the pedestrian detection performance, especially sheltered pedestrians in an image. The method includes acquiring a training data set, a test data set and pedestrian labels of a pedestrian detection image; using the training data set for training to obtain a detection model by using a pedestrian detection method, and acquiring initial pedestrian boundary box and confidence degrees and coordinates thereof; performing Gaussian penalty on the confidence degrees of the pedestrian boundary box, to obtain confidence degree of the pedestrian boundary box after the penalty; and obtaining final pedestrian boundary boxes by screening the pedestrian boundary boxes. Thus, repeated boundary boxes of a single pedestrian are removed while reserving boundary boxes of sheltered pedestrians, thereby realizing the detection of the pedestrians in an image.
机译:公开了一种通过使用高斯罚款检测图像中行人的方法。使用高斯罚款筛选初始行人边界盒,以改善图像中的行人检测性能,特别是图像中的庇护行人。该方法包括获取训练数据集,一个人行检测图像的测试数据集和行人标签;使用培训数据集进行培训,通过使用行人检测方法来获得检测模型,并获取初始行人边界盒及其置信度和坐标;在行人边界盒的置信度上表演高斯罚款,以获得罚款后行人边界箱的置信度;通过筛选行人边界盒获得最终行人边界盒。因此,在保留遮蔽行人的边界盒的同时除去单个行人的重复边界盒,从而实现了图像中的行人的检测。

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