论文标题
静态图像中的基于插件的恢复人群计数
Plug-and-Play Rescaling Based Crowd Counting in Static Images
论文作者
论文摘要
人群计数是一个具有挑战性的问题，尤其是在图像越来越多的人群多样性和复杂的混乱人群般的背景区域的情况下，大多数以前的方法都无法很好地概括，因此引起了巨大的人群低估或高估。为了应对这些挑战，我们提出了一个新的图像补丁恢复模块（PRM）和三个独立的PRM采用人群计数方法。所提出的框架使用PRM模块来重新销售需要特殊处理的图像区域（贴片），而分类过程有助于识别和丢弃任何可能导致高估的混杂人群样背景区域。对三个标准基准和跨数据库评估的实验表明，我们的方法在RMSE评估指标中的最先进模型的表现高达10.4％，并且具有卓越的新数据集能力。
Crowd counting is a challenging problem especially in the presence of huge crowd diversity across images and complex cluttered crowd-like background regions, where most previous approaches do not generalize well and consequently produce either huge crowd underestimation or overestimation. To address these challenges, we propose a new image patch rescaling module (PRM) and three independent PRM employed crowd counting methods. The proposed frameworks use the PRM module to rescale the image regions (patches) that require special treatment, whereas the classification process helps in recognizing and discarding any cluttered crowd-like background regions which may result in overestimation. Experiments on three standard benchmarks and cross-dataset evaluation show that our approach outperforms the state-of-the-art models in the RMSE evaluation metric with an improvement up to 10.4%, and possesses superior generalization ability to new datasets.
