A crowd density forecasting task aims to predict how the crowd density map will change in the future from observed past crowd density maps. However, the past crowd density maps are often incomplete due to the miss-detection of pedestrians, and it is crucial to develop a robust crowd density forecasting model against the miss-detection. This paper presents a MAsked crowd density Completion framework for crowd density forecasting (CrowdMAC), which is simultaneously trained to forecast future crowd density maps from partially masked past crowd density maps (i.e., forecasting maps from past maps with miss-detection) while reconstructing the masked observation maps (i.e., imputing past maps with miss-detection). Additionally, we propose Temporal-Density-aware Masking (TDM), which non-uniformly masks tokens in the observed crowd density map, considering the sparsity of the crowd density maps and the informativeness of the subsequent frames for the forecasting task. Moreover, we introduce multi-task masking to enhance training efficiency. In the experiments, CrowdMAC achieves state-of-the-art performance on seven large-scale datasets, including SDD, ETH-UCY, inD, JRDB, VSCrowd, FDST, and croHD. We also demonstrate the robustness of the proposed method against both synthetic and realistic miss-detections. The code is released at https://fujiry0.github.io/CrowdMAC-project-page.

翻译：人群密度预测任务旨在根据观测到的历史人群密度图预测未来人群密度图的变化。然而，由于行人漏检，历史人群密度图往往不完整，因此开发对漏检具有鲁棒性的人群密度预测模型至关重要。本文提出了一种用于人群密度预测的掩码人群密度补全框架（CrowdMAC），该框架通过联合训练实现两个目标：从部分掩码的历史人群密度图（即存在漏检的历史图）预测未来人群密度图，同时重构被掩码的观测图（即对存在漏检的历史图进行填补）。此外，考虑到人群密度图的稀疏性以及后续帧对预测任务的信息价值，我们提出了时序密度感知掩码（TDM）方法，对观测到的人群密度图中的令牌进行非均匀掩码。进一步地，我们引入多任务掩码机制以提升训练效率。实验表明，CrowdMAC在七个大规模数据集（包括SDD、ETH-UCY、inD、JRDB、VSCrowd、FDST和croHD）上取得了最先进的性能。我们还验证了所提方法对合成与真实漏检场景的鲁棒性。代码发布于 https://fujiry0.github.io/CrowdMAC-project-page。