Person search aims to search for a target person among multiple images recorded by multiple surveillance cameras, which faces various challenges from both pedestrian detection and person re-identification. Besides the large intra-class variations owing to various illumination conditions, occlusions and varying poses, background clutters in the detected pedestrian bounding boxes further deteriorate the extracted features for each person, making them less discriminative. To tackle these problems, we develop a novel approach which guides the network with segmentation masks so that discriminative features can be learned invariant to the background clutters. We demonstrate that joint optimization of pedestrian detection, person re-identification and pedestrian segmentation enables to produce more discriminative features for pedestrian, and consequently leads to better person search performance. Extensive experiments on two widely used benchmark datasets prove the superiority of our approach. In particular, our proposed model achieves the state-of-the-art performance (86.3% mAP and 86.5% top-1 accuracy) on CUHK-SYSU dataset.

人员搜索旨在在多个监控摄像机记录的多个图像中搜索目标人员，这面临着来自行人检测和人员重新识别的各种挑战。除了由于各种照明条件，遮挡和姿势变化而导致的较大的组内变化之外，检测到的行人边界盒中的背景杂波进一步恶化了每个人的提取特征，从而使它们的辨别力降低。为了解决这些问题，我们开发了一种新颖的方法，该方法可以使用分割蒙版引导网络，以便可以根据背景杂波来学习区分特征。我们证明，对行人检测，人员重新识别和行人分割进行联合优化可以为行人产生更多区分特征，从而提高了人员搜索性能。在两个广泛使用的基准数据集上进行的大量实验证明了我们方法的优越性。尤其是，我们提出的模型在CUHK-SYSU数据集上实现了最先进的性能（86.3％的mAP和86.5％的top-1准确性）。