看这篇，用深度信息做视频数人数，拥挤遮挡也不虚，MAE 降了 62%，代码和数据集都开源了。
本论文提出深度引导检测器 DG-Det，结合多尺度 RGB-D 交叉注意力和显式遮挡预测，增强拥挤与遮挡场景下的目标检测能力。同时引入统一去重框架，消除跨帧重复计数。作者发布了带深度信息的 RGB-D 视频计数数据集，涵盖多类目标。相比现有基线，该方法在 MAE 上降低 62.01%，RMSE 也取得一致改善。
Depth-Guided Video Object Counting in Crowded Scenes
Our primary objective is to advance video object counting in crowded scenes, aiming to robustly count all instances of a target category based on given text or visual prompts. Existing methods rely on RGB information, limiting their discriminative ability in crowded and occluded conditions. To address this, we propose a Depth-Guided Detector (DG-Det) along with a general post-processing pipeline. By integrating depth cues with multi-scale RGB-D cross-attention and explicit occlusion prediction, our method enhances spatial understanding and achieves robust detection in crowded and occluded scenes. Furthermore, we introduce a unified de-duplication framework to eliminate cross-frame redundant counting. To facilitate future research, we also release a new RGB-D Video Object Counting dataset featuring depth information and multiple object categories persequence. Extensive experiments demonstrate that our method achieves a 62.01\% reduction in MAE compared to existing baselines, and also produces consistent improvements in RMSE. We provide the source code at https://github.com/streamer-AP/DG-Net and the dataset at https://huggingface.co/datasets/aerospace123/RGBD-VideoCount.