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Joint Person Objectness and Repulsion for Person Search

机译:人员搜索的联合人物对象和排斥

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摘要

Person search targets to search the probe person from the unconstrainted scene images, which can be treated as the combination of person detection and person matching. However, the existing methods based on the Detection-Matching framework ignore the person objectness and repulsion (OR) which are both beneficial to reduce the effect of distractor images. In this paper, we propose an OR similarity by jointly considering the objectness and repulsion information. Besides the traditional visual similarity term, the OR similarity also contains an objectness term and a repulsion term. The objectness term can reduce the similarity of distractor images that not contain a person and boost the performance of person search by improving the ranking of positive samples. Because the probe person has a different person ID with its neighbors, the gallery images having a higher similarity with the neighbors of probe should have a lower similarity with the probe person. Based on this repulsion constraint, the repulsion term is proposed to reduce the similarity of distractor images that are not most similar to the probe person. Treating the Faster R-CNN as the person detector, the OR similarity is evaluated on PRW and CUHK-SYSU datasets by the Detection-Matching framework with six description models. The extensive experiments demonstrate that the proposed OR similarity can effectively reduce the similarity of distractor samples and further boost the performance of person search, e.g., improve the mAP from 92.32% to 93.23% for CUHK-SYSY dataset, and from 50.91% to 52.30% for PRW datasets.
机译:人员搜索目标从非共度的场景图像中搜索探测人员,这可以被视为人员检测和人匹配的组合。然而,基于检测匹配框架的现有方法忽视了人们对象和排斥(或),这既有利于减少触干镜头的效果。在本文中,我们通过共同考虑对象和排斥信息来提出或相似。除了传统的视觉相似项之外,或相似性还包含对象项和排斥项。客观术语可以减少不包含一个人的患者图像的相似性,并通过改善正样品的排名来提高人员搜索的性能。因为探测人员具有与其邻居的不同人ID,所以与探针的邻居具有更高相似性的图库相似应该具有较低的相似性。基于这种排斥约束,提出了排斥项,以减少与探针人最相似的分散运动图像的相似性。用六个描述模型的检测匹配框架对PRW和Cuhk-Sysu数据集进行评估更快的R-CNN作为人检测器,或相似度。广泛的实验表明,提出的或相似性可以有效地降低了分散的样本的相似性,并进一步提高了人搜索的性能,例如,从92.32%到93.23%的Cuhk-sysy数据集,从50.91%到52.30%对于prw datasets。

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