DC Field | Value | Language |
dc.contributor.author | Ihnatsyeva, I. | - |
dc.contributor.author | Bohush, R. | - |
dc.coverage.spatial | Минск | en_US |
dc.date.accessioned | 2024-03-01T07:32:48Z | - |
dc.date.available | 2024-03-01T07:32:48Z | - |
dc.date.issued | 2023 | - |
dc.identifier.citation | Ihnatsyeva, I. Person re-identification using compound descriptor and invisible region replacement / I. Ihnatsyeva, R. Bohush // Pattern Recognition and Information Processing (PRIP'2023) = Распознавание образов и обработка информации (2023) : Proceedings of the 16th International Conference, October 17–19, 2023, Minsk, Belarus / United Institute of Informatics Problems of the National Academy of Sciences of Belarus. – Minsk, 2023. – P. 193–196. | en_US |
dc.identifier.uri | https://libeldoc.bsuir.by/handle/123456789/54445 | - |
dc.description.abstract | In this paper we proposed person re-identification
algorithm using compound descriptor that includes global and
local features for the top, middle, and bottom of the person
figure. Local areas are formed based on the person figure key
points coordinates. If there are not enough visible points, the
area is recognized as invisible and feature vector corresponding
component is replaced by an average value for the k-nearest
neighbors. Testing was performed on datasets for re-
identification Market-1501, DukeMTMC-ReID, MSMT17,
PolReID1077. Our algorithm allows us to increase accuracy re-
identification for metric Rank1 by 8 - 51% and for metric mAP
by 28 - 97% relative to the baseline. | en_US |
dc.language.iso | en | en_US |
dc.publisher | BSU | en_US |
dc.subject | материалы конференций | en_US |
dc.subject | convolution neuron networks | en_US |
dc.subject | PolReID1077 | en_US |
dc.subject | occlusion | en_US |
dc.title | Person re-identification using compound descriptor and invisible region replacement | en_US |
dc.type | Article | en_US |
Appears in Collections: | Pattern Recognition and Information Processing (PRIP'2023) = Распознавание образов и обработка информации (2023)
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