Given a target image as query, person re-identification systems retrieve a ranked list of candidate matches on a per-camera basis. In deployed systems, a human operator scans these lists and labels sighted targets by touch or mouse-based selection. However, classical re-id approaches generate per-camera lists independently. Therefore, target identifications by operator in a subset of cameras cannot be utilized to improve ranking of the target in remaining set of network cameras. To address this shortcoming, we propose a novel sequential multi-camera re-id approach. The proposed approach can accommodate human operator inputs and provides early gains via a monotonic improvement in target ranking. At the heart of our approach is a fusion function which operates on deep feature representations of query and candidate matches. We formulate an optimization procedure custom-designed to incrementally improve query representation. Since existing evaluation methods cannot be directly adopted to our setting, we also propose two novel evaluation protocols. The results on two large-scale re-id datasets (Market-1501, DukeMTMC-reID) demonstrate that our multi-camera method significantly outperforms baselines and other popular feature fusion schemes. Additionally, we conduct a comparative subject-based study of human operator performance. The superior operator performance enabled by our approach makes a compelling case for its integration into deployable video-surveillance systems.
翻译:根据作为查询的目标图像,个人再身份识别系统将每个相机检索一份排名的候选人名单。在部署的系统中,一个人类操作员通过触摸或鼠标选择,扫描这些名单和标签的视觉目标。然而,典型的重新定位方法独立生成每个相机名单。因此,无法利用一组照相机操作员在一组照相机中的目标识别方法来提高剩余一组网络摄像机中的目标排序。为解决这一缺陷,我们提议了一个新的连续多镜头再定位方法。拟议方法可以容纳人类操作员的投入,并通过目标排序的单调改进来提供早期收益。我们的方法的核心是结合功能,在查询和候选人匹配的深度特征显示上运作。我们设计了一个优化的定制程序,以逐步改进查询代表性。由于现有评价方法不能直接用于我们的环境,我们还提议了两个新的评价协议。两个大规模再定位数据集(Market-1501,DukMMMC-reID)的结果显示,我们的多相机方法大大超越了目标排序的基线,并且通过其他通用的特征整合方法提供了早期收益。我们通过一个基于操作员的图像整合系统进行一项具有说服力的比较性的业绩分析。