Depth-Guided Video Object Counting in Crowded Scenes
Quick summary
arXiv:2608.06236v1 Announce Type: cross 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 achi
Key takeaways
- arXiv:2608.06236v1 Announce Type: cross 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.
Why it matters
The importance of “Depth-Guided Video Object Counting in Crowded Scenes” will be measured by what changes in practice. User behavior, access conditions, verifiable performance and responsible-use outcomes are the signals worth following.
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