- Tytuł:
- Person re-identification accuracy improvement by training a CNN with the new large joint dataset and re-rank
- Autorzy:
-
Bohush, Rykhard
Ihnatsyeva, Sviatlana
Ablameyko, Sergey - Powiązania:
- https://bibliotekanauki.pl/articles/2201263.pdf
- Data publikacji:
- 2022
- Wydawca:
- Szkoła Główna Gospodarstwa Wiejskiego w Warszawie. Instytut Informatyki Technicznej
- Tematy:
-
convolution neural network
PolReID
re-identification
large-scale dataset
re-rank - Opis:
- The paper is aimed to improve person re-identification accuracy in distributed video surveillance systems based on constructing a large joint image dataset of people for training convolutional neural networks (CNN). For this aim, an analysis of existing datasets is provided. Then, a new large joint dataset for person re-identification task is constructed that includes the existing public datasets CUHK02, CUHK03, Market, Duke, MSMT17 and PolReID. Testing for re-identification is performed for such frequently cited CNNs as ResNet-50, DenseNet121 and PCB. Re-identification accuracy is evaluated by using the main metrics Rank, mAP and mINP. The use of the new large joint dataset makes it possible to improve Rank1 mAP, mINP on all test sets. Re-ranking is used to further increase the re-identification accuracy. Presented results confirm the effectiveness of the proposed approach.
- Źródło:
-
Machine Graphics & Vision; 2022, 31, 1/4; 93--109
1230-0535
2720-250X - Pojawia się w:
- Machine Graphics & Vision
- Dostawca treści:
- Biblioteka Nauki
Artykuł