Person re-identification with expanded neighborhoods distance re-ranking

摘要

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.