Consistent cross-view matching with global camera-network constraints improves unsupervised video person re-identification, reaching 65.3% rank-1 on MARS and 76.5% on DukeMTMC-VideoReID.
Video-based person re-identification via self-paced learning and deep reinforcement learning framework,
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Exploiting Global Camera Network Constraints for Unsupervised Video Person Re-identification
Consistent cross-view matching with global camera-network constraints improves unsupervised video person re-identification, reaching 65.3% rank-1 on MARS and 76.5% on DukeMTMC-VideoReID.