在人群密度估计中捕获尺度不变上下文的扩展卷积

Thishen Packirisamy, Richard Klein
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引用次数: 0

摘要

人群密度估算(CDE)可以通过防止踩踏事件或减少疾病传播来确保人群安全,这在Covid-19的兴起中变得迫在眉睫。由于诸如遮挡和大规模变化等问题,CDE是一个具有挑战性的问题。本研究旨在创建、评估和比较不同的人群计数方法,重点关注扩展卷积提取尺度不变上下文信息的能力。在这项工作中,我们建立并训练了三种不同的模型架构:一个没有扩张的卷积神经网络(CNN),一个有扩张的CNN来捕捉上下文,一个有空间金字塔池(ASPP)层来捕捉尺度不变的上下文特征。我们对每个架构进行了多次训练,以确保统计显著性,并使用上海科技和UCF CC 50数据集上的均方误差(MSE)、平均误差(MAE)和网格平均绝对误差(GAME)对它们进行评估。比较两种方法的结果,我们发现将扩展卷积应用于更稀疏的人群图像,规模变化较小,不会产生显着差异,但在高度拥挤的人群图像上,扩展卷积对遮挡更具弹性,表现更好。此外,我们发现在人群中对象的规模存在显著差异的情况下,添加ASPP层可以提高性能。这项研究的代码可在https://github.com/ThishenP/crowd-density上获得。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Dilated Convolution to Capture Scale Invariant Context in Crowd Density Estimation
Crowd Density Estimation (CDE) can be used ensure safety of crowds by preventing stampedes or reducing spread of disease which was made urgent with the rise of Covid-19. CDE a challenging problem due to problems such as occlusion and massive scale varia- tions. This research looks to create, evaluate and compare different approaches to crowd counting focusing on the ability for dilated convolution to extract scale-invariant contex- tual information. In this work we build and train three different model architectures: a Convolutional Neural Network (CNN) without dilation, a CNN with dilation to capture context and a CNN with an Atrous Spatial Pyramid Pooling (ASPP) layer to capture scale-invariant contextual features. We train each architecture multiple times to ensure statistical significance and evaluate them using the Mean Squared Error (MSE), Mean Average Error (MAE) and Grid Average Mean Absolute Error (GAME) on the Shang- haiTech and UCF CC 50 datasets. Comparing the results between approaches we find that applying dilated convolution to more sparse crowd images with little scale variations does not make a significant difference but, on highly congested crowd images, dilated con- volutions are more resilient to occlusion and perform better. Furthermore, we find that adding an ASPP layer improves performance in the case when there are significant differ- ences in the scale of objects within the crowds. The code for this research is available at https://github.com/ThishenP/crowd-density.
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