异构分支关联特征融合的行人重识别

Person Re-identification Based on Heterogeneous Branch Correlative Features Fusion

针对行人重识别(Person Re-ID)过程中,多分支结构的网络模型在提取行人特征时缺乏异构特征的问题,提出一种异构分支关联特征融合的行人重识别算法。训练阶段,将OSNet与注意力机制相结合作为主干共享网络,以学习到具有更强显著性和区分性的行人关键特征;将分支网络输出的行人特征进行水平均等分割,再提取关联条纹特征,从而全面利用位于条纹间的综合信息;设计异构特征提取模块,以增加模型学习差异特征所需的结构多样性。推理阶段,将多个特征向量融合成一个新的特征向量,再进行相似性判断。将该方法在Market-1501、DukeMTMC-reID数据集上进行有效性实验验证并对结果进行分析,所提算法能够提高行人重识别的准确率,模型所提取的特征具有较强的鲁棒性和判别力。

Most of the multi-branch network based person re-identification(Person Re-ID)methods face the problem of lack of heterogeneous features in the procedure of extraction of pedestrian features.In this paper,a novel Person Re-ID algorithm based on heterogeneous branch correlative features fusion is proposed.In the training stage,the attention-based OSNet is designed as the backbone sharing network,which can extract more significant and distinguished key features.The pedestrian features from branch network are segmented equally in the vertical axis.The relevant stripe features are extracted to utilize the synthesis information between different stripes.The heterogeneous features extraction module is designed to increase the structural diversity of the model for learning difference features.In the inference stage,multiple feature vectors are fused into a new feature vector,and the similarity judgment is performed.The effectiveness of the proposed algorithm is verified by experiments on Market-1501 and DukeMTMC-reID datasets,and the experiment results are analyzed.The proposed algorithm can improve the accuracy of Person Re-ID,and the features extracted by the model have strong robustness and discriminability.