Improving Person Re-Identification by Adaptive Hard Sample Mining

Chen, Kezhou; Chen, Yang; Han, Chuchu; Sang, Nong; Wang, Ruolin

doi:10.1109/icip.2018.8451129

Cited by 3 publications

(1 citation statement)

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“…The hard positive samples and the hard negative samples are together called to hard samples in ReID. Some methods of hard sample mining which improve to recognize hard samples are proposed, such as [1] and [2]. [3] focuses on hard negative mining which improve to recognize hard negative samples by using the proposed triplet loss.…”

Section: Introductionmentioning

confidence: 99%

Applying hard positive mining and its evaluation for person re-identification

Hiroi

Kameyama

2020

IEICE ComEX

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Person Re-identification" technology has attracted more attention because of the proliferation of surveillance cameras and the increased awareness of crime prevention. However, there are various problems. One of them is that the same person is likely to be recognized erroneously as a different person due to the differences in persons' appearance. Therefore, in this paper, we propose a method of hard positive mining to achieve better performance in identifying the same person images that are difficult to identify as the same person. The evaluation results using MARS dataset show that the CMS by applying the proposed hard positive mining is higher than the existing method of CNN with temporal pooling. It suggests that it is effective to apply hard positive mining in MARS dataset.

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Section: Introductionmentioning

confidence: 99%