论文标题
密集的人群检测和用轻量级的体系结构进行计数
Dense Crowds Detection and Counting with a Lightweight Architecture
论文作者
论文摘要
在人群计数的背景下，大多数作品都集中在提高准确性，而不考虑导致不适合嵌入式应用程序的算法的性能。在本文中，我们提出了一种轻巧的卷积神经网络体系结构，以使用较少的计算机资源进行人群检测和计数，而没有明显的计数准确性损失。使用贝叶斯损失功能对架构进行了训练，以进一步提高其准确性，然后修剪以进一步减少所使用的计算资源。在USF-QNRF上测试了拟议的架构，以达到154.07的竞争平均平均误差，均值均方根误差为241.77，同时保持了竞争性的参数为0.067亿。获得的结果表明，贝叶斯损失可以与其他架构一起进一步改善它们，而最后一个卷积层也没有提供重要的信息，甚至鼓励在训练中过度拟合。
In the context of crowd counting, most of the works have focused on improving the accuracy without regard to the performance leading to algorithms that are not suitable for embedded applications. In this paper, we propose a lightweight convolutional neural network architecture to perform crowd detection and counting using fewer computer resources without a significant loss on count accuracy. The architecture was trained using the Bayes loss function to further improve its accuracy and then pruned to further reduce the computational resources used. The proposed architecture was tested over the USF-QNRF achieving a competitive Mean Average Error of 154.07 and a superior Mean Square Error of 241.77 while maintaining a competitive number of parameters of 0.067 Million. The obtained results suggest that the Bayes loss can be used with other architectures to further improve them and also the last convolutional layer provides no significant information and even encourage over-fitting at training.
