Yu Yang, Huang Jifeng, Du Wen, Xiong Naixue
College of Information, Mechanical and Electrical Engineering, Shanghai Normal University, Shanghai 201418, China.
DS Information Technology Co., Ltd., Shanghai 200032, China.
Sensors (Basel). 2019 Apr 29;19(9):2013. doi: 10.3390/s19092013.
Crowd counting, which is widely used in disaster management, traffic monitoring, and other fields of urban security, is a challenging task that is attracting increasing interest from researchers. For better accuracy, most methods have attempted to handle the scale variation explicitly. which results in huge scale changes of the object size. However, earlier methods based on convolutional neural networks (CNN) have focused primarily on improving accuracy while ignoring the complexity of the model. This paper proposes a novel method based on a lightweight CNN-based network for estimating crowd counting and generating density maps under resource constraints. The network is composed of three components: a basic feature extractor (BFE), a stacked à trous convolution module (SACM), and a context fusion module (CFM). The BFE encodes basic feature information with reduced spatial resolution for further refining. Various pieces of contextual information are generated through a short pipeline in SACM. To generate a context fusion density map, CFM distills feature maps from the above components. The whole network is trained in an end-to-end fashion and uses a compression factor to restrict its size. Experiments on three highly-challenging datasets demonstrate that the proposed method delivers attractive performance.
人群计数在灾害管理、交通监控和城市安全等其他领域有着广泛应用，是一项具有挑战性的任务，正吸引着研究人员越来越多的关注。为了获得更高的准确性，大多数方法都试图显式地处理尺度变化，这导致了目标尺寸的巨大尺度变化。然而，早期基于卷积神经网络（CNN）的方法主要集中在提高准确性上，而忽略了模型的复杂性。本文提出了一种基于轻量级CNN网络的新方法，用于在资源受限的情况下估计人群数量并生成密度图。该网络由三个组件组成：一个基本特征提取器（BFE）、一个堆叠空洞卷积模块（SACM）和一个上下文融合模块（CFM）。BFE以降低的空间分辨率对基本特征信息进行编码，以便进一步细化。通过SACM中的一个短管道生成各种上下文信息。为了生成上下文融合密度图，CFM从上述组件中提取特征图。整个网络以端到端的方式进行训练，并使用一个压缩因子来限制其大小。在三个极具挑战性的数据集上进行的实验表明，所提出的方法具有出色的性能。
