Person Search in Videos with One Portrait Through Visual and Temporal Links
This addresses a practical need in law enforcement and video retrieval by improving person search in diverse environments, though it is an incremental advancement over existing re-identification methods.
The paper tackles the problem of searching for a person in long videos using only a single portrait, which is challenging due to environmental differences, and proposes a framework that leverages visual and temporal links to propagate identities, achieving a mAP increase from 42.16% to 62.27%.
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%.