拥挤场景中基于深度引导的视频目标计数
Depth-Guided Video Object Counting in Crowded Scenes
- Harbin Institute of Technology (Weihai)（哈尔滨工业大学（威海））
- City University of Hong Kong（香港城市大学）
- University of Science and Technology of China（中国科学技术大学）
- Harbin Institute of Technology Qingdao Research Institute（哈尔滨工业大学青岛研究院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
该研究针对拥挤场景视频目标计数的局限性，提出DG-Det与后处理流水线、去重框架，发布新数据集，使MAE降62.01%且RMSE提升。
AI中文摘要：
我们的主要目标是推进拥挤场景下的视频目标计数，旨在基于给定的文本或视觉提示稳健计数目标类别的所有实例。现有方法依赖RGB信息，在拥挤和遮挡条件下判别能力受限。为解决该问题，我们提出了深度引导检测器（DG-Det）及通用后处理流水线。通过将深度线索与多尺度RGB-D交叉注意力、显式遮挡预测相结合，我们的方法提升了空间理解能力，在拥挤和遮挡场景中实现稳健检测。此外，我们引入统一去重框架以消除跨帧冗余计数。为推动未来研究，我们还发布了新的RGB-D视频目标计数数据集，包含深度信息及每序列多个目标类别。大量实验表明，与现有基线相比，我们的方法使平均绝对误差（MAE）降低62.01%，均方根误差（RMSE）也取得持续提升。我们在该httpsURL提供了源代码，在该httpsURL提供了数据集。
英文摘要：
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.
