Person Re-ID has been gaining a lot of attention and nowadays is of fundamental importance in many camera surveillance applications. The task consists of identifying individuals across multiple cameras that have no overlapping views. Most of the approaches require labeled data, which is not always available, given the huge amount of demanded data and the difficulty of manually assigning a class for each individual. Recently, studies have shown that re-ranking methods are capable of achieving significant gains, especially in the absence of labeled data. Besides that, the fusion of feature extractors and multiple-source training is another promising research direction not extensively exploited. We aim to fill this gap through a manifold rank aggregation approach capable of exploiting the complementarity of different person Re-ID rankers. In this work, we perform a completely unsupervised selection and fusion of diverse ranked lists obtained from multiple and diverse feature extractors. Among the contributions, this work proposes a query performance prediction measure that models the relationship among images considering a hypergraph structure and does not require the use of any labeled data. Expressive gains were obtained in four datasets commonly used for person Re-ID. We achieved results competitive to the state-of-the-art in most of the scenarios.

翻译：行人重识别（Person Re-ID）近年来备受关注，现已成为众多摄像监控应用中的核心任务。该任务旨在识别多个无重叠视野摄像头中的同一行人。现有方法大多依赖标注数据，然而由于所需数据量巨大且难以手动为每个行人分配类别标签，标注数据往往难以获取。近期研究表明，重排序方法能在无标注数据情况下显著提升性能。此外，特征提取器融合与多源训练是另一条未被充分挖掘的具有前景的研究方向。本文旨在通过一种流形排序聚合方法填补这一空白，该方法能利用不同行人重识别排序器之间的互补性。本工作对来自多种特征提取器的异质排序列表进行了完全无监督的选择与融合。其中，本文提出了一种查询性能预测度量，该度量基于超图结构建模图像间关联，无需使用任何标注数据。在四个常用的行人重识别数据集上，本方法取得了显著的性能提升。在多数场景中，我们的结果达到了与现有最先进方法相竞争的水平。