Person Re identification: Past, Present and Future
Person re identification (re ID) has become increasingly popular in the community due to its application and research significance. It aims at spotting a person of interest in other cameras. In the early days, hand crafted algorithms and small scale evaluation were predominantly reported. Recent years have witnessed the emergence of large scale datasets and deep learning systems which make use of large data volumes. Considering different tasks, we classify most current re ID methods into two classes, i.e. image based and video based; in both tasks, hand crafted and deep learning systems will be reviewed. Moreover, two new re ID tasks which are much closer to real world applications are described and discussed, i.e. end to end re ID and fast re ID in very large galleries. This paper: 1) introduces the history of person re ID and its relationship with image classification and instance retrieval; 2) surveys a broad selection of the hand crafted systems and the large scale methods in both image and video based re ID; 3) describes critical future directions in end to end re ID and fast retrieval in large galleries; and 4) finally briefs some important yet under developed issues.
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