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Multi-scale multi-patch person re-identification with exclusivity regularized softmax
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DOI：10.1016/j.neucom.2019.11.062.png)
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Abstract
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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. (C) 2019 Elsevier B.V. All rights reserved.
Keywords:
Person re-identification
Deep learning
Exclusivity regularized softmax
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