Crowd counting has been applied to a variety of applications such as video surveillance, traffic monitoring, assembly control, and other public safety applications. Context information, such as perspective distortion and background interference, is a crucial factor in achieving high performance for crowd counting. While traditional methods focus merely on solving one specific factor, we aggregate sufficient context information into the crowd counting network to tackle these problems simultaneously in this study. We build a fully convolutional network with two tasks, i.e., main density map estimation and auxiliary semantic segmentation. The main task is to extract the multi-scale and spatial context information to learn the density map. The auxiliary semantic segmentation task gives a comprehensive view of the background and foreground information, and the extracted information is finally incorporated into the main task by late fusion. We demonstrate that our network has better accuracy of estimation and higher robustness on three challenging datasets compared with state-of-the-art methods.

人群计数已应用于各种应用程序，例如视频监视，交通监控，组装控制和其他公共安全应用程序。上下文信息（例如透视失真和背景干扰）是实现高性能的人群计数的关键因素。尽管传统方法仅专注于解决一个特定因素，但在本研究中，我们将足够的上下文信息汇总到人群计数网络中以同时解决这些问题。我们建立了一个具有两个任务的全卷积网络，即主密度图估计和辅助语义分割。主要任务是提取多尺度和空间上下文信息以学习密度图。辅助语义分割任务可提供有关背景和前景信息的全面视图，最后通过后期融合将提取的信息纳入主要任务。我们证明，与最新方法相比，我们的网络在三个具有挑战性的数据集上具有更好的估计准确性和更高的鲁棒性。