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      Person Search in Videos with One Portrait Through Visual and Temporal Links

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          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%.

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          Most cited references 31

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          Deep Residual Learning for Image Recognition

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            Microsoft COCO: Common Objects in Context

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              Scalable Person Re-identification: A Benchmark

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                Author and article information

                Journal
                27 July 2018
                Article
                1807.10510

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

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                European Conference on Computer Vision (ECCV), 2018
                cs.CV

                Computer vision & Pattern recognition

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