Abstract. In real-world applications, e.g. law enforcement and video retrieval, one often needs to search a certain person in long videos with just
one portrait. This is much more challenging than the conventional settings for person re-identification, as the search may need to be carried out
in the environments different from where the portrait was taken. In this
paper, we aim to tackle this challenge and propose a novel framework,
which takes into account the identity invariance along a tracklet, thus
allowing person identities to be propagated via both the visual and the
temporal links. We also develop a novel scheme called Progressive Propagation via Competitive Consensus, which significantly improves the reliability of the propagation process. To promote the study of person search,
we construct a large-scale benchmark, which contains 127K manually annotated tracklets from 192 movies. Experiments show that our approach
remarkably outperforms mainstream person re-id methods, raising the
mAP from 42.16% to 62.27%