Abstract
An effective and efficient person re-identification (ReID) algorithm will alleviate painful video watching, and accelerate the investigation progress. Recently, with the explosive requirements of practical applications, a lot of research efforts have been dedicated to heterogeneous person re-identification (He-ReID). In this paper, we review the state-of-the-art methods comprehensively with respect to four main application scenarios -- low-resolution, infrared, sketch and text. We begin with a comparison between He-ReID and the general Homogeneous ReID (Ho-ReID) task. Then, we survey the models that have been widely employed in He-ReID. Available existing datasets for performing evaluation are briefly described. We then summarize and compare the representative approaches. Finally, we discuss some future research directions.
Abstract (translated)
一种有效的人再识别（REID）算法可以减轻观看视频的痛苦，加快调查进程。近年来，随着实际应用的爆炸性要求，许多研究工作致力于异类人的再鉴定（何瑞德）。本文从低分辨率、红外、素描和文本四个主要应用场景出发，对目前最先进的方法进行了综合评述。我们首先比较了He-Reid和一般齐次Reid（Ho-Reid）任务。然后，我们调查了赫里德广泛使用的模型。简要描述了用于执行评估的现有数据集。然后，我们总结并比较具有代表性的方法。最后，讨论了未来的研究方向。
URL
https://arxiv.org/abs/1905.10048
