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.

翻译：人群密度预测任务旨在根据观测到的过去人群密度图预测未来人群密度图的变化。然而，由于行人的漏检，过去的人群密度图往往不完整，因此开发一种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），该框架同时训练以从部分掩码的过去人群密度图（即从存在漏检的过去地图预测未来地图）预测未来人群密度图，同时重建被掩码的观测图（即对存在漏检的过去地图进行填补）。此外，考虑到人群密度图的稀疏性以及后续帧对于预测任务的信息量，我们提出了时空密度感知掩码（TDM），该方法非均匀地对观测人群密度图中的标记进行掩码。此外，我们引入了多任务掩码以提高训练效率。在实验中，CrowdMAC在七个大规模数据集上取得了最先进的性能，包括SDD、ETH-UCY、inD、JRDB、VSCrowd、FDST和croHD。我们还证明了所提方法对合成和真实漏检均具有鲁棒性。