论文标题
基于建立的人群计数 - 快速保持准确
Inception-Based Crowd Counting -- Being Fast while Remaining Accurate
论文作者
论文摘要
最近，基于CNN的算法最近证明了它们的非凡能力，可以自动从图像中计数人群，这要归功于其结构，这些结构旨在解决各种头尺度的问题。但是，这些复杂的体系结构也大大提高了计算复杂性，使实时估计令人难以置信。因此，在本文中，提出了一种基于Inception-V3的新方法来减少计算量。这种提出的方法（ICC）利用了前五个启动块和在CAN中设计的上下文模块，以在不同的接收场上提取特征，从而是上下文感知的。这两种不同策略的使用也可以提高模型的鲁棒性。实验表明，ICC最多可以减少85.3％的计算，而绩效损失为24.4％。这种高效率对监视系统中的人群计数模型的部署产生了重大贡献，以保护公共安全。该代码将在https://github.com/yimingma/crowdcounting-icc及其在人群计数数据集中进行的预训练的权重，该数据集从监视的角度来看，这也将开放为代码。
Recent sophisticated CNN-based algorithms have demonstrated their extraordinary ability to automate counting crowds from images, thanks to their structures which are designed to address the issue of various head scales. However, these complicated architectures also increase computational complexity enormously, making real-time estimation implausible. Thus, in this paper, a new method, based on Inception-V3, is proposed to reduce the amount of computation. This proposed approach (ICC), exploits the first five inception blocks and the contextual module designed in CAN to extract features at different receptive fields, thereby being context-aware. The employment of these two different strategies can also increase the model's robustness. Experiments show that ICC can at best reduce 85.3 percent calculations with 24.4 percent performance loss. This high efficiency contributes significantly to the deployment of crowd counting models in surveillance systems to guard the public safety. The code will be available at https://github.com/YIMINGMA/CrowdCounting-ICC,and its pre-trained weights on the Crowd Counting dataset, which comprises a large variety of scenes from surveillance perspectives, will also open-sourced.
