ArXiv

Depth-Guided Video Object Counting in Crowded Scenes

2026-08-06 · 16:24 UTC ·Yuanjing Xu, Xinyan Liu, Weidong Chen... ·1 min read
Authors
Yuanjing Xu, Xinyan Liu, Weidong Chen...
Categories
cs.CV, cs.AI
arXiv
https://arxiv.org/abs/2608.06236v1
PDF
https://arxiv.org/pdf/2608.06236v1

Brief

Depth-Guided Detector (DG-Det) advances video object counting in crowded, occluded scenes by fusing depth with RGB via multi-scale RGB‑D cross-attention and explicit occlusion prediction; experiments show a 62.01% MAE reduction and consistent RMSE gains versus RGB-only baselines. The authors also introduce a unified de-duplication step, release an RGB‑D Video Object Counting dataset and code, and were accepted to ACM Multimedia 2026.

Why it matters

Depth-Guided Detector (DG-Det) integrates depth cues with multi-scale RGB-D cross-attention and explicit occlusion prediction, producing a 62.01% reduction in MAE and consistent RMSE improvements compared to prior RGB-only baselines.

Key details

  • The paper adds a unified de-duplication framework to remove cross-frame redundant counts, releases an RGB-D Video Object Counting dataset (multiple object categories per sequence) and code (GitHub and HuggingFace links), and was accepted to ACM Multimedia 2026.
Source evidence

Abstract

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.

Comment: Accepted at ACM Multimedia 2026