Summary of Bound Tightening Network For Robust Crowd Counting, by Qiming Wu
Bound Tightening Network for Robust Crowd Counting
by Qiming Wu
First submitted to arxiv on: 27 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel approach to Robust Crowd Counting, aiming to estimate the number of individuals in crowded images or videos from surveillance cameras. The Bound Tightening Network (BTN) architecture consists of three parts: base model, smooth regularization module, and certify bound module. By propagating interval bounds through the base model and utilizing layer weights for guidance, BTN improves both counting accuracy and robustness. Experimental results on benchmark datasets demonstrate the effectiveness and efficiency of this approach. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps count people in crowded pictures or videos from cameras. The problem is important because accurate counting can help with things like crowd management or emergency response. The new method, called Bound Tightening Network (BTN), does better than other methods by making sure the count is correct and not fooled by noise or distortion. |
Keywords
» Artificial intelligence » Regularization
