电子科技大学学报2024，Vol.53Issue(2)：235-242,8.DOI:10.12178/1001-0548.2023056
多因素引导的行人重识别数据增广方法研究
Research on Pedestrian Re-Identification Data Augmentation Method Based on Multi-Factor Guidance
摘要
Abstract
To solve the difficulty in obtaining annotated pedestrian images in the field of pedestrian re-identification research,a novel data augmentation method guided by multi-factor is proposed in this paper.Firstly,a local multi-scale guidance mechanism is designed in the generator network.It can suppress the local artifacts in generated images through feature fusion.Secondly,a long-distance correlation guidance mechanism is proposed to improve the overall visual quality of the generated pedestrian image by guiding the long-distance dependence of the generated image with external attention.Lastly,an adversarial discrimination network is designed and embed into original generative adversarial networks.The three network stability architecture model increases the stability of generative adversarial network training.The experiment are validated on the VIPeR,Market-1501 and DukeMTMC-reID benchmark datasets.The results demonstrate our method outperforms the state-of-the-art with the mAP and rank-1 scores,especially in small-scale datasets.关键词
行人重识别/生成对抗网络/数据增广/局部多尺度/注意力机制Key words
person re-identification/generative adversial network/data augmentation/local multi-scale/attention mechanism分类
信息技术与安全科学引用本文复制引用
刘志刚,张国辉,高月,刘苗苗..多因素引导的行人重识别数据增广方法研究[J].电子科技大学学报,2024,53(2):235-242,8.基金项目
国家自然科学基金(51774090,42002138) （51774090,42002138）
黑龙江省自然科学基金(LH2020F003) （LH2020F003）
河北省自然科学基金(D2023107002) （D2023107002）
黑龙江省属本科高校团队创新基金(2022TSTD-03) （2022TSTD-03）
黑龙江省高等教育教学改革项目(SJGY20210109) （SJGY20210109）
