Crowd Counting Algorithm Based on Feature Pyramid Network

doi:10.19678/j.issn.1000-3428.0050951

Abstract

Abstract: The single-picture crowd count has a sharp decline in performance due to severe population occlusion and scale changes.Therefore,this paper proposes an algorithm for crowd counting pictures,and gives a Full Convolution Network(FCN) capable of processing the resolution of any picture.The scale change and occlusion problems in the picture are solved by applying the feature pyramid network to the crowd count.The network model is trained and evaluated in the crowd counting database ShanghaiTech,results show that the algorithm has good robustness and accuracy compared with the current mainstream crowd counting algorithm.

Key words: crowd counting, Convolutional Neural Network(CNN), feature pyramid, density map, Mean Absolute Error(MAE)

摘要： 由于单张图片人群计数存在严重的人群遮挡和尺度变化问题，导致人群计数算法性能明显下降。为此，提出一种基于特征金字塔网络对图片进行人群计数的算法，并给出能够处理任意图片分辨率的全卷积网络。将特征金字塔网络应用到人群计数中，通过逐层融合网络中不同尺度的特征图来解决图片中的上述问题。在人群计数数据库ShanghaiTech上对网络模型进行训练和性能评测，结果表明，与当前主流的人群计数算法相比，该算法具有更高的鲁棒性和准确性。

关键词: 人群计数, 卷积神经网络, 特征金字塔, 密度图, 平均绝对误差

CLC Number:

TP18

MA Hao, YIN Baoqun, PENG Sifan. Crowd Counting Algorithm Based on Feature Pyramid Network[J]. Computer Engineering, 2019, 45(7): 203-207.

马皓, 殷保群, 彭思凡. 基于特征金字塔网络的人群计数算法[J]. 计算机工程, 2019, 45(7): 203-207.

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