利用深度信息提升拥挤场景下视频目标计数的准确性。
Depth-Guided Video Object Counting in Crowded Scenes
- 融合深度与多尺度图像注意力，增强遮挡下的空间理解。
- 相比基线方法，MAE降低62.01%，RMSE也显著改善。
- 适用于需要精准计数的交通、安防等密集场景应用。
本文旨在提升拥挤场景中视频目标计数的鲁棒性，基于文本或视觉提示准确统计目标类别实例数量。现有方法依赖RGB信息，在遮挡和密集场景中区分能力有限。为此，我们提出深度引导检测器（DG-Det）及通用后处理流程，通过结合深度线索与多尺度RGB-D交叉注意力，并显式预测遮挡关系，增强空间感知能力，实现复杂场景下的稳定检测。此外，引入统一去重框架，消除跨帧重复计数。为促进研究，我们发布了一个新数据集RGBD-VideoCount，包含深度信息与每序列多类别标注。大量实验表明，本方法相较基线实现62.01%的MAE降低，同时在RMSE上也有持续改进。代码与数据已开源。
原文摘要 · Abstract (English)
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.
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