Dynamic Label Graph Matching for Unsupervised Video Re-Identification

ICCV 2017  ·  Mang Ye, Andy J. Ma, Liang Zheng, Jiawei Li, P C Yuen ·

Label estimation is an important component in an unsupervised person re-identification (re-ID) system. This paper focuses on cross-camera label estimation, which can be subsequently used in feature learning to learn robust re-ID models. Specifically, we propose to construct a graph for samples in each camera, and then graph matching scheme is introduced for cross-camera labeling association. While labels directly output from existing graph matching methods may be noisy and inaccurate due to significant cross-camera variations, this paper proposes a dynamic graph matching (DGM) method. DGM iteratively updates the image graph and the label estimation process by learning a better feature space with intermediate estimated labels. DGM is advantageous in two aspects: 1) the accuracy of estimated labels is improved significantly with the iterations; 2) DGM is robust to noisy initial training data. Extensive experiments conducted on three benchmarks including the large-scale MARS dataset show that DGM yields competitive performance to fully supervised baselines, and outperforms competing unsupervised learning methods.

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Datasets


Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Person Re-Identification PRID2011 DGM+IDE+ Rank-1 56.4 # 9
Rank-20 96.4 # 9
Rank-5 81.3 # 8
Person Re-Identification PRID2011 DGM+MLAPG+ Rank-1 73.1 # 8
Rank-20 99.0 # 7
Rank-5 92.5 # 6

Methods


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