提出新模型提升人群计数的鲁棒性，防止误判。
Bound Tightening Network for Robust Crowd Counting
- 通过区间传播与层权重引导网络学习
- 在多个数据集上实现更高准确率与效率
- 适合关注模型可信度的安防场景
人群计数是基础任务，旨在估计监控摄像头输入的密集图像或视频中的人数。近期研究聚焦于提升计数精度，却忽略了计数模型的可认证鲁棒性。本文提出一种新型边界收紧网络（Bound Tightening Network, BTN），由基础模型、平滑正则化模块和认证边界模块三部分组成。核心思想是将区间边界通过基础模型（认证边界模块）进行传播，并利用层权重（平滑正则化模块）指导网络学习。在多个基准数据集上的实验表明，BTN 在有效性与效率方面均表现优异。
原文摘要 · Abstract (English)
Crowd Counting is a fundamental topic, aiming to estimate the number of individuals in the crowded images or videos fed from surveillance cameras. Recent works focus on improving counting accuracy, while ignoring the certified robustness of counting models. In this paper, we propose a novel Bound Tightening Network (BTN) for Robust Crowd Counting. It consists of three parts: base model, smooth regularization module and certify bound module. The core idea is to propagate the interval bound through the base model (certify bound module) and utilize the layer weights (smooth regularization module) to guide the network learning. Experiments on different benchmark datasets for counting demonstrate the effectiveness and efficiency of BTN.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。