In the person re-identification (re-ID) community, pedestrians often have great changes in appearance, and there are many similar persons, which incurs will degrades the accuracy. Re-ranking is an effective method to solve these problems, this paper proposes an expanded neighborhoods distance (END) to re-rank the re-ID results. We assume that if the two persons in different image are same, their initial ranking lists and two-level neighborhoods will be very similar when they are taken as the query. Our method follows the principle of similarity, and selects expanded neighborhoods in initial ranking list to calculate the END distance. Final distance is calculated as the combination of the END distance and Jaccard distance. Experiments on Market-1501, DukeMTMC-reID and CUHK03 datasets confirm the effectiveness of the novel re-ranking method in this article. Compare with re-ID baseline, the proposed method in this paper increases mAP by 14.2% on Market-1501 and Rank1 by 12.9% on DukeMTMC-reID.

在人员重新识别（re-ID）社区中，行人经常在外观上有很大的变化，并且有很多相似的人，这会导致准确性下降。重新排序是解决这些问题的有效方法，本文提出了一种扩展的邻域距离（END）来对重新ID结果进行重新排序。我们假设如果两个人在不同的图像中是相同的，那么当他们被用作查询时，他们的初始排名列表和两级邻域将非常相似。我们的方法遵循相似性原理，并在初始排名列表中选择扩展的邻域来计算END距离。最终距离由END距离和Jaccard距离的组合计算得出。在Market-1501上进行的实验，DukeMTMC-reID和CUHK03数据集证实了本文中新的重新排名方法的有效性。与re-ID基准相比，本文提出的方法在Market-1501上将mAP提高了14.2％，在DukeMTMC-reID上将Rank1提高了12.9％。