论文标题
人群计数和无监督的人本地化的强大基准
A Strong Baseline for Crowd Counting and Unsupervised People Localization
论文作者
论文摘要
在本文中，我们探索了人群计数的强大基线，并且基于估计的密度图的无监督人本地化算法。首先，现有方法基于不同的骨干和各种训练技巧来实现最先进的性能。我们收集不同的骨干和训练技巧，并评估改变它们的影响并开发有效的人群计数管道，从而在多个数据集上大大降低了MAE和RMSE。我们还提出了一种名为孤立的kmeans的聚类算法，以在密度图中定位头部。此方法可以将密度图分为子区域，并在无需训练任何参数的情况下找到局部计数约束的中心，并且可以轻松地与现有方法集成。
In this paper, we explore a strong baseline for crowd counting and an unsupervised people localization algorithm based on estimated density maps. Firstly, existing methods achieve state-of-the-art performance based on different backbones and kinds of training tricks. We collect different backbones and training tricks and evaluate the impact of changing them and develop an efficient pipeline for crowd counting, which decreases MAE and RMSE significantly on multiple datasets. We also propose a clustering algorithm named isolated KMeans to locate the heads in density maps. This method can divide the density maps into subregions and find the centers under local count constraints without training any parameter and can be integrated with existing methods easily.
