论文标题
自适应混合物回归网络，具有本地计数图，用于人群计数
Adaptive Mixture Regression Network with Local Counting Map for Crowd Counting
论文作者
论文摘要
人群计算任务旨在估计图像或视频中的框架中的人数。现有方法广泛采用密度图作为训练目标，以优化点对点损失。在测试阶段，我们仅关注人群数量与全球密度图之间的差异，这表明训练目标与评估标准之间的不一致。为了解决这个问题，我们引入了一个新目标，称为本地计数图（LCM），以获得比基于密度图的方法更准确的结果。此外，我们还提出了一个以粗到精细的方式使用三个模块的自适应混合物回归框架，以进一步提高人群估计的精度：比例感知模块（SAM），混合物回归模块（MRM）和自适应软间隔模块（ASIM）。具体而言，Sam完全利用了来自不同卷积特征的上下文和多尺度信息。 MRM和ASIM对局部图像斑块进行更精确的计数回归。与当前方法相比，提出的方法在典型数据集上报告了更好的性能。源代码可从https://github.com/xiyang1012/local-crowd-counting获得。
The crowd counting task aims at estimating the number of people located in an image or a frame from videos. Existing methods widely adopt density maps as the training targets to optimize the point-to-point loss. While in testing phase, we only focus on the differences between the crowd numbers and the global summation of density maps, which indicate the inconsistency between the training targets and the evaluation criteria. To solve this problem, we introduce a new target, named local counting map (LCM), to obtain more accurate results than density map based approaches. Moreover, we also propose an adaptive mixture regression framework with three modules in a coarse-to-fine manner to further improve the precision of the crowd estimation: scale-aware module (SAM), mixture regression module (MRM) and adaptive soft interval module (ASIM). Specifically, SAM fully utilizes the context and multi-scale information from different convolutional features; MRM and ASIM perform more precise counting regression on local patches of images. Compared with current methods, the proposed method reports better performances on the typical datasets. The source code is available at https://github.com/xiyang1012/Local-Crowd-Counting.
