Horizontal Pyramid Matching for Person Re-identification

Abstract

Despite the remarkable recent progress, person re-identification (Re-ID) approaches are still suffering from the failure cases where the discriminative body parts are missing. To mitigate such cases, we propose a simple yet effective Horizontal Pyramid Matching (HPM) approach to fully exploit various partial information of a given person, so that correct person candidates can be still identified even even some key parts are missing. Within the HPM, we make the following contributions to produce a more robust feature representation for the Re-ID task: 1) we learn to classify using partial feature representations at different horizontal pyramid scales, which successfully enhance the discriminative capabilities of various person parts; 2) we exploit average and max pooling strategies to account for person-specific discriminative information in a global-local manner. To validate the effectiveness of the proposed HPM, extensive experiments are conducted on three popular benchmarks, including Market-1501, DukeMTMC-ReID and CUHK03. In particular, we achieve mAP scores of 83.1%, 74.5% and 59.7% on these benchmarks, which are the new state-of-the-arts. Our code is available on Github

Accepted by AAAI 2019

References in corpus (17)

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Cited by in corpus (7)

• STA: Spatial-Temporal Attention for Large-Scale Video-based Person Re-Identification
• GaitSet: Regarding Gait as a Set for Cross-View Gait Recognition
• Adversarial Metric Attack and Defense for Person Re-identification
• Attention: A Big Surprise for Cross-Domain Person Re-Identification
• Multi-Scale Body-Part Mask Guided Attention for Person Re-identification
• VMRFANet:View-Specific Multi-Receptive Field Attention Network for Person Re-identification
• Push for Center Learning via Orthogonalization and Subspace Masking for Person Re-Identification