Person search is of great significance to public safety research, such as crime surveillance, video surveillance and security. Person search is a method of locating and identifying the queried person from a complete set of images. The main cause of false recall and missed detection in person search is the presence of person occlusion in the images. In order to improve the accuracy of person search when the person to be queried is occluded, this paper proposes an attention-based one-stage framework for person search (ABOS) using an anchor-free model as a baseline. The method uses the channel attention module to express different forms of occlusion and take full advantage of the spatial attention module to highlight the target region of the occluded pedestrians. These attention modules integrate deep and shallow features to guide the network to pay attention to the visible area of the occluded target and extract the semantic information of the pedestrians. Experimental results on CUHK-SYSU and PRW datasets show that the proposed person search method based on attention mechanism in this paper has better performance than existing methods, achieving 93.7\(\%\) of mAP on CUHK-SYSU dataset and 46.4\(\%\) of mAP on PRW dataset, respectively.

人员搜索对于公共安全研究具有重要意义，例如犯罪监控、视频监控和安全。人员搜索是一种从一组完整的图像中定位和识别被查询人员的方法。人物搜索中错误召回和漏检的主要原因是图像中存在人物遮挡。为了提高被查询人被遮挡时的行人搜索准确率，本文提出了一种基于注意力的单阶段行人搜索框架（ABOS），以无锚模型为基线。该方法使用通道注意力模块来表达不同形式的遮挡，并充分利用空间注意力模块来突出被遮挡行人的目标区域。这些注意力模块融合了深浅特征来引导网络关注被遮挡目标的可见区域并提取行人的语义信息。在 CUHK-SYSU 和 PRW 数据集上的实验结果表明，本文提出的基于注意力机制的人员搜索方法比现有方法具有更好的性能，达到 93.7CUHK-SYSU数据集上mAP的\( \ %\)和PRW数据集上mAP的46.4 \(\%\) ，分别。