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Gallery based k-reciprocal-like re-ranking for heavy cross-camera discrepancy in person re-identification

2018; Elsevier BV; Volume: 333; Linguagem: Inglês

10.1016/j.neucom.2018.12.055

1872-8286

Haijun Liu, Jian Cheng,

Gait Recognition and Analysis

Recently more and more attention is paid to the re-ranking step for person re-identification in computer vision community, especially those fully automatic, unsupervised solutions. Among them, k-reciprocal re-ranking (KR) method achieved big success. However, when the heavy cross-camera discrepancy exists between query and gallery datasets, it may degrade the performance. To alleviate the heavy cross-camera discrepancy between query and gallery datasets, we propose a gallery based k-reciprocal-like re-ranking (GKR) method. GKR adopts graph matching to construct the matching correspondence between query and gallery datasets. Then the proposed k-reciprocal-like neighbors are computed only on gallery dataset instead of on the union of query and gallery datasets like KR does. Moreover, to perform unsupervised video-based person re-identification, we incorporate our proposed GKR method into the dynamic label graph matching (DGM) framework, which can improve the cross-camera labels estimating in training step but also can improve the re-identification accuracy by re-ranking in testing step. Experimental results by supervised and unsupervised solutions on some benchmarks, demonstrate the effectiveness of our GKR method to handle the cross-camera discrepancy problem for person re-identification.