Self-Erasing Network for Person Re-Identification

Clicks: 111
ID: 269271
2021
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Abstract
Person re-identification (ReID) plays an important role in intelligent surveillance and receives widespread attention from academics and the industry. Due to extreme changes in viewing angles, some discriminative local regions are suppressed. In addition, the data with similar backgrounds collected by a fixed viewing angle camera will also affect the model’s ability to distinguish a person. Therefore, we need to discover more fine-grained information to form the overall characteristics of each identity. The proposed self-erasing network structure composed of three branches benefits the extraction of global information, the suppression of background noise and the mining of local information. The two self-erasing strategies that we proposed encourage the network to focus on foreground information and strengthen the model’s ability to encode weak features so as to form more effective and richer visual cues of a person. Extensive experiments show that the proposed method is competitive with the advanced methods and achieves state-of-the-art performance on DukeMTMC-ReID and CUHK-03(D) datasets. Furthermore, it can be seen from the activation map that the proposed method is beneficial to spread the attention to the whole body. Both metrics and the activation map validate the effectiveness of our proposed method.
Reference Key
fan2021sensorsself-erasing Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Xinyue Fan;Yang Lin;Chaoxi Zhang;Jia Zhang;Fan, Xinyue;Lin, Yang;Zhang, Chaoxi;Zhang, Jia;
Journal sensors
Year 2021
DOI
10.3390/s21134262
URL
https://doi.org/10.3390/s21134262
Keywords
Deep learning person re-identification background suppression maximum activation suppression

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