Crowd localization, predicting head positions, is a more practical and high-level task than simply counting. Existing methods employ pseudo-bounding boxes or pre-designed localization maps, relying on complex post-processing to obtain the head positions. In this paper, we propose an elegant, end-to-end Crowd Localization TRansformer named CLTR that solves the task in the regression-based paradigm. The proposed method views the crowd localization as a direct set prediction problem, taking extracted features and trainable embeddings as input of the transformer-decoder. To achieve good matching results, we introduce a KMO-based Hungarian, which innovatively revisits the label assignment from a context view instead of an independent instance view. Extensive experiments conducted on five datasets in various data settings show the effectiveness of our method. In particular, the proposed method achieves the best localization performance on the NWPU-Crowd, UCF-QNRF, and ShanghaiTech Part A datasets.

本文提出了一种名为CLTR的Crowd Localization Transformer，采用回归范式解决权限更高的预测头部位置任务，并使用基于KMO的匈牙利匹配器来减少模糊点和生成更合理的匹配结果，实验结果表明本方法在多个数据集上效果较好，特别是在NWPU-Crowd数据集上表现最佳。

一种端到端的Transformer模型用于人群定位