Crowd counting is one of the most challenging issues in the computer vision community for safety and security through surveillance systems. It has extensive range of applications, such as disaster management, surveillance event detection, intelligence gathering and analysis, public safety control, traffic monitoring, design of public spaces, anomaly detection and military. Early approaches still encounter many issues like non-uniform density distribution, partial occlusion and discrepancies in scale and perspective. To address the above problems, feature pyramid networks are introduced in deep convolution networks for counting the individuals in the crowd. The designed network has extracted the features at all resolutions and is constructed rapidly from only one input image. This method achieves outperformance results compared to the well-known networks on three standard crowd counting datasets.

人群计数是计算机视觉社区中最具挑战性的问题之一，通过监控系统实现安全和保障。它具有广泛的应用，如灾害管理、监视事件检测、情报收集和分析、公共安全控制、交通监控、公共空间设计、异常检测和军事。早期的方法仍然遇到许多问题，例如密度分布不​​均匀、部分遮挡以及尺度和视角的差异。为了解决上述问题，在深度卷积网络中引入了特征金字塔网络来计算人群中的个体。设计的网络提取了所有分辨率的特征，并且仅从一张输入图像中快速构建。