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Towards Open-World Person Re-Identification by One-Shot Group-Based Verification

机译:通过基于小组的一枪验证来实现对开放世界人员的重新识别

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摘要

Solving the problem of matching people across non-overlapping multi-camera views, known as person re-identification (re-id), has received increasing interests in computer vision. In a real-world application scenario, a watch-list (gallery set) of a handful of known target people are provided with very few (in many cases only a single) image(s) (shots) per target. Existing re-id methods are largely unsuitable to address this open-world re-id challenge because they are designed for (1) a closed-world scenario where the gallery and probe sets are assumed to contain exactly the same people, (2) person-wise identification whereby the model attempts to verify exhaustively against each individual in the gallery set, and (3) learning a matching model using multi-shots. In this paper, a novel transfer local relative distance comparison (t-LRDC) model is formulated to address the open-world person re-identification problem by one-shot group-based verification. The model is designed to mine and transfer useful information from a labelled open-world non-target dataset. Extensive experiments demonstrate that the proposed approach outperforms both non-transfer learning and existing transfer learning based re-id methods.
机译:解决跨非重叠多摄像机视图匹配人的问题,即人重新识别(re-id),已引起人们对计算机视觉的越来越多的关注。在实际应用场景中,为几个已知目标人员的监视列表(画廊集)提供的每个目标很少(很多情况下只有一个)图像(快照)。现有的re-id方法在很大程度上不适合解决这种开放世界的re-id挑战,因为它们设计用于(1)假设画廊和探针集包含完全相同的人的封闭世界场景,(2)明智的识别,借此模型尝试对画廊集中的每个人进行详尽的验证,以及(3)使用多次射击学习匹配的模型。本文提出了一种新颖的转移局部相对距离比较(t-LRDC)模型,通过基于一次验证的小组验证来解决开放世界人员的重新识别问题。该模型旨在从标记的开放世界非目标数据集中挖掘和传输有用的信息。大量实验表明,该方法优于非转移学习和现有的基于转移学习的re-id方法。

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