多尺度融合与双输出U-Net网络的行人重识别

Multi-Scale Fusion and Dual Output U-Net Network for Person Re-Identification

受行人姿态的多变性和行人被遮挡等因素的影响,行人重识别模型难以提取行人关键特征。为增强模型的特征表达能力,提出一种基于多尺度融合与双输出U-Net网络的行人重识别方法,旨在解决现有方法中难以提取行人关键特征、特征表达能力较低的问题。首先,提出多尺度融合的双输出U-Net网络,并对输出特征进行欧氏距离和散度距离约束;其次,设计联合损失函数,解决生成对抗网络在训练过程中不易收敛的问题,提高训练过程的收敛速度。在3个公共基准数据集上的仿真实验结果表明,相比经典特征提取网络,所提特征提取网络的平均精度均值(mAP)提升超过10%,所提行人重识别方法相比主流方法的mAP提高约2%,该方法能够增强模型的特征表达能力,提高行人重识别的准确率。

Due to variable pedestrian posture,the occlusion of pedestrians,and other adverse factors,Person Re-Identification(Re-ID)models often struggle to extract the key features of pedestrians.To enhance the feature expression ability of the model,this study proposes a Re-ID method based on multi-scale fusion and a dual output U-Net network.The aim is to address the challenges of extracting key pedestrian features and improving feature expression,which are limitations of existing methods.First,a multi-scale fusion dual output U-Net network is proposed,with the output features constrained by both Euclidean and divergence distances.Second,a joint loss function is introduced to address the challenge of the Generative Adversarial Network(GAN)not converging easily during training,thereby improving the convergence speed of the training process.Numerous simulation experiments conducted on three public reference datasets demonstrate that the proposed feature extraction network outperforms classical feature extraction networks,with an improvement in Mean Average Precision(mAP)of over 10%.In addition,the mAP of the proposed Re-ID method improves by approximately 2%compared to that of the mainstream method.The proposed method can enhance the feature expression ability of the model and improve the accuracy of Re-ID.