Abstract
Designing real-world person re-identification (re-id) systems requires attention to operational aspects not typically considered in academic research. Typically, the probe image or image sequence is matched to a gallery set with a fixed candidate list. On the other hand, in real-world applications of re-id, we would search for a person of interest in a gallery set that is continuously populated by new candidates over time. A key question of interest for the operator of such a system is: how long is a correct match to a probe likely to remain in a rank-k shortlist of candidates? In this paper, we propose to distill this information into what we call a Rank Persistence Curve (RPC), which unlike a conventional cumulative match characteristic (CMC) curve helps directly compare the temporal performance of different re-id algorithms. To carefully illustrate the concept, we collected a new multi-shot person re-id dataset called RPIfield. The RPIfield dataset is constructed using a network of 12 cameras with 112 explicitly time-stamped actor paths among about 4000 distractors. We then evaluate the temporal performance of different re-id algorithms using the proposed RPCs using single and pairwise camera videos from RPIfield, and discuss considerations for future research.
Abstract (translated)
设计真实世界人员重新识别（重新识别）系统需要注意学术研究中通常不考虑的操作方面。通常，探测图像或图像序列与具有固定候选列表的图库集匹配。另一方面，在re-id的真实世界应用中，我们将在随着时间的推移不断填充新候选人的图库集中搜索感兴趣的人。对这样一个系统的运营商感兴趣的一个关键问题是：与可能保留在候选人的k级候选名单中的探测器的正确匹配有多长？在本文中，我们建议将这些信息提炼为我们称之为秩持久性曲线（RPC），与传统的累积匹配特性（CMC）曲线不同，它有助于直接比较不同重新算法的时间性能。为了仔细说明这个概念，我们收集了一个名为RPIfield的新的多人重新数据集。 RPIfield数据集是使用12个摄像机的网络构建的，在大约4000个干扰物中有112个显式时间戳的演员路径。然后，我们使用来自RPIfield的单对和成对摄像机视频，使用提出的RPC评估不同re-id算法的时间性能，并讨论未来研究的考虑因素。
URL
https://arxiv.org/abs/1808.05499
