Person Re-Identification with Feature Pyramid Optimization and Gradual Background Suppression.

Clicks: 158
ID: 95547
2020
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Ranked #45 of 55 articles by views in neural networks : the official journal of the international neural network society

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Abstract
Compared with face recognition, the performance of person re-identification (re-ID) is still far from practical application. Among various interferences, there are two factors seriously limiting the performance improvement, i.e., the feature discriminability determined by "external network effectiveness", and the image quality determined by "internal background clutters". Target at the "external network effectiveness" problem, feature pyramids are effective to learn discriminative features because they can learn both detailed features from high-resolution shallow layers and semantical features from low-resolution deep layers, however, it can only achieve slight improvement on re-ID tasks because of the error back propagation problem. To handle the problem and utilize the effectiveness of feature pyramids, we propose a strategy called Feature Pyramid Optimization (FPO). Instead of concatenating features directly, the selected layers are optimized independently in a top-bottom order. Target at the "internal background clutters" problem, background suppression is generally considered for removing the environmental interference and improving the image quality. Several mask-based methods are used attempting to totally remove background clutters but achieve limited promotion because of the mask sharpening effect. We propose a novel strategy, i.e., Gradual Background Suppression (GBS) to reduce the background clutters and keep the smoothness of images simultaneously. Extensive experiments have been conducted and the results demonstrate the effectiveness of both FPO and GBS.
Reference Key
tang2020personneural Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Tang, Yingzhi;Yang, Xi;Wang, Nannan;Song, Bin;Gao, Xinbo;
Journal neural networks : the official journal of the international neural network society
Year 2020
DOI
S0893-6080(20)30014-9
URL
Keywords
end-to-end feature pyramid optimization gradual background suppression person re-identification

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