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

为解决行人重识别研究领域中行人标注图像获取困难的问题,提出一种多因素引导的行人数据增广方法。首先,在生成器网络中设计了一种局部多尺度引导机制,通过特征融合抑制生成图像的局部伪影;其次,提出了长距离相关性引导机制,通过外注意力引导生成图像的长距离依赖,提高生成行人图像的整体视感质量;最后,提出一种抗博弈判别网络,通过嵌入到生成对抗网络,从而构建一种三网络稳定博弈架构模型,增加生成对抗网络训练的稳定性。通过VIPeR、Market-1501、DukeMTMC-reID这3种不同规模数据集的仿真实验,结果表明该方法与目前主流方法相比,mAP与Rank-1精度上均有不同程度的提升,在小规模数据集上的提升较为显著。

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.