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Multi-scale multi-patch person re-identification with exclusivity regularized softmax.
- Source :
-
Neurocomputing . Mar2020, Vol. 382, p64-70. 7p. - Publication Year :
- 2020
-
Abstract
- Discriminative feature learning is critical for person re-identification. To obtain abundant visual information from the input person image, we first propose a novel network that extracts multi-scale patch-level deep features. Then, we propose an improved softmax loss function for learning more compact and more discriminative feature vectors. Specifically, we integrate feature pyramid blocks and region-level global average pooling functions into the feature extraction network, introduce the well-established normalization techniques in face recognition algorithms into person re-ID, and penalize the redundancy in feature vectors by minimizing the l 1,2 norm of the weight matrix in the softmax layer. Experiments on three large-scale datasets under the standard settings show the effectiveness of the proposed method. Moreover, we report our cross-domain re-ID results by training re-ID models on source datasets and testing them on other target datasets. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 09252312
- Volume :
- 382
- Database :
- Academic Search Index
- Journal :
- Neurocomputing
- Publication Type :
- Academic Journal
- Accession number :
- 141607867
- Full Text :
- https://doi.org/10.1016/j.neucom.2019.11.062