Body Symmetry and Part-Locality-Guided Direct Nonparametric Deep Feature Enhancement for Person Reidentification
| Autor: | Jingchang Huang, Canhui Cai, Zhen Lei, Lixin Zheng, Xiaobin Zhu, Jianqing Zhu, Huanqiang Zeng |
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| Rok vydání: | 2020 |
| Předmět: |
Normalization (statistics)
Computer Networks and Communications Computer science business.industry Deep learning Locality Pattern recognition 02 engineering and technology 010501 environmental sciences 01 natural sciences Convolutional neural network Computer Science Applications Hardware and Architecture Feature (computer vision) Face (geometry) Signal Processing 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business Feature learning Similarity learning 0105 earth and related environmental sciences Information Systems |
| Zdroj: | IEEE Internet of Things Journal. 7:2053-2065 |
| ISSN: | 2372-2541 |
| Popis: | In recent years, deep learning (DL) has been successfully and widely applied in the person reidentification (Re-ID). However, the DL-based person Re-ID methods face a bottleneck that the scales of most existing person Re-ID databases are not large enough for training very deep models. To address this problem, a body symmetry and part-locality-guided direct nonparametric deep feature enhancement (DNDFE) method is proposed in this article. Based on the observation that the body symmetry and part locality are two important appearance properties inherited in the upright walking persons, the proposed method designs two nonparametric layers, namely, the body symmetry average pooling and local normalization layers, to construct a DNDFE module to well explore the body symmetry and part locality properties. The proposed DNDFE module could be directly embedded between the traditional deep feature learning module and similarity learning module to enhance the DL features so as to improve the person Re-ID performance. The experimental results have shown that the proposed DNDFE method is superior to multiple state-of-the-art person Re-ID methods in terms of accuracy and efficiency. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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