论文标题
多尺度人群对多任务点监督进行计数和本地化
Multiscale Crowd Counting and Localization By Multitask Point Supervision
论文作者
论文摘要
我们建议在统一框架中进行人群计数和人本地化的多任务方法。由于检测和本地化任务相关并且可以共同解决，因此我们的模型通过学习编码人群图像的多尺度表示并随后将其融合来从多任务解决方案中受益。与相对流行的基于密度的方法相反，我们的模型使用点监督来准确识别人群的位置。我们在两个受欢迎的人群计数数据集A和B上测试了我们的模型，并证明我们的方法在计数和本地化任务上都取得了强大的成果，MSE的测量为110.7和15.0，人群计数和AP的测量为0.71和0.75的本地化，分别在Shanghaitech A和B上。我们的详细消融实验显示了我们的多尺度方法的影响以及嵌入网络中的融合模块的有效性。我们的代码可在以下网址提供：https：//github.com/rcvlab-aiimlab/crowd_counting。
We propose a multitask approach for crowd counting and person localization in a unified framework. As the detection and localization tasks are well-correlated and can be jointly tackled, our model benefits from a multitask solution by learning multiscale representations of encoded crowd images, and subsequently fusing them. In contrast to the relatively more popular density-based methods, our model uses point supervision to allow for crowd locations to be accurately identified. We test our model on two popular crowd counting datasets, ShanghaiTech A and B, and demonstrate that our method achieves strong results on both counting and localization tasks, with MSE measures of 110.7 and 15.0 for crowd counting and AP measures of 0.71 and 0.75 for localization, on ShanghaiTech A and B respectively. Our detailed ablation experiments show the impact of our multiscale approach as well as the effectiveness of the fusion module embedded in our network. Our code is available at: https://github.com/RCVLab-AiimLab/crowd_counting.
