Horizontal Pyramid Matching for Person Re-identification
This work improves person re-identification for security and surveillance applications by enhancing robustness to missing body parts, though it is incremental as it builds on existing methods.
The paper tackles the problem of person re-identification (Re-ID) by addressing cases where discriminative body parts are missing, proposing a Horizontal Pyramid Matching (HPM) approach that achieves new state-of-the-art mAP scores of 83.1%, 74.5%, and 59.7% on three benchmarks.
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