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AlignedReID: Surpassing Human-Level Performance in Person Re-Identification

2017-11-22Code Available0· sign in to hype

Xuan Zhang, Hao Luo, Xing Fan, Weilai Xiang, Yixiao Sun, Qiqi Xiao, Wei Jiang, Chi Zhang, Jian Sun

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Abstract

In this paper, we propose a novel method called AlignedReID that extracts a global feature which is jointly learned with local features. Global feature learning benefits greatly from local feature learning, which performs an alignment/matching by calculating the shortest path between two sets of local features, without requiring extra supervision. After the joint learning, we only keep the global feature to compute the similarities between images. Our method achieves rank-1 accuracy of 94.4% on Market1501 and 97.8% on CUHK03, outperforming state-of-the-art methods by a large margin. We also evaluate human-level performance and demonstrate that our method is the first to surpass human-level performance on Market1501 and CUHK03, two widely used Person ReID datasets.

Tasks

Person Re-Identification

Benchmark Results

DatasetModelMetricClaimedVerifiedStatus
CUHK03AlignedReID (RK)Rank-197.8—Unverified
CUHK03-CAligned++ Rank-17.99—Unverified
CUHK-SYSUAlignedReIDMAP94.4—Unverified
Market-1501AlignedReID (RK)Rank-194.4—Unverified
Market-1501-CAligned++ Rank-131—Unverified

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