CrowdMAC：用于鲁棒人群密度预测的掩码人群密度补全
CrowdMAC: Masked Crowd Density Completion for Robust Crowd Density Forecasting
AI总结：
针对人群密度预测中过去密度图因检测遗漏不完整的问题，提出CrowdMAC框架，通过同时训练预测未来图和重建掩码观测图，结合TDM非均匀掩码，在7个数据集上取得最优性能并验证对检测遗漏的鲁棒性。
摘要：
人群密度预测任务旨在从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，由于行人检测遗漏，过去的人群密度图往往不完整，因此开发针对检测遗漏具有鲁棒性的人群密度预测模型至关重要。本文提出一种用于人群密度预测的掩码人群密度补全框架（CrowdMAC），该框架同时进行训练，从部分掩码的过去人群密度图（即从存在检测遗漏的过去图）预测未来人群密度图，同时重建掩码的观测图（即对存在检测遗漏的过去图进行补全）。此外，我们提出时间-密度感知掩码（TDM），该方法考虑人群密度图的稀疏性和后续帧对预测任务的信息性，对观测人群密度图中的标记进行非均匀掩码。在实验中，CrowdMAC在SDD、ETH-UCY、inD、JRDB、VSCrowd、FDST和croHD这7个大规模数据集上取得了最先进的性能。我们还证明了所提方法对合成和真实检测遗漏的鲁棒性。代码已发布于https://fujiry0.github.io/CrowdMAC-project-page。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
