Automatic estimation of the number of people in unconstrained crowded scenes is a challenging task and one major difficulty stems from the huge scale variation of people. In this paper, we propose a novel Deep Structured Scale Integration Network (DSSINet) for crowd counting, which addresses the scale variation of people by using structured feature representation learning and hierarchically structured loss function optimization. Unlike conventional methods which directly fuse multiple features with weighted average or concatenation, we first introduce a Structured Feature Enhancement Module based on conditional random fields (CRFs) to refine multiscale features mutually with a message passing mechanism. In this module, each scale-specific feature is considered as a continuous random variable and passes complementary information to refine the features at other scales. Second, we utilize a Dilated Multiscale Structural Similarity loss to enforce our DSSINet to learn the local correlation of people's scales within regions of various size, thus yielding high-quality density maps. Extensive experiments on four challenging benchmarks well demonstrate the effectiveness of our method. Specifically, our DSSINet achieves improvements of 9.5% error reduction on Shanghaitech dataset and 24.9% on UCF-QNRF dataset against the state-of-the-art methods.

本文提出了一种基于Deep Structured Scale Integration Network (DSSINet) 的人群计数新方法，通过结构化特征表征学习和分层结构损失函数优化处理人的尺度变化，并利用多尺度特征和条件随机场进行特征增强，通过空洞多尺度结构相似性损失函数，从不同尺度的区域中学习人的局部相关性。实验验证表明，该方法能显著提高 Shanghaitech数据集和UCF-QNRF数据集上的算法精度。

基于深度结构化尺度融合网络的人群计数