基于特征拼接的行人重识别

Person Re-Identification Based on Feature Stitching

基于卷积神经网络算法对行人进行多特征提取,并使用拼接后的多特征对行人进行特征表征。使用全局池化和多个卷积构建多分支结构,利用多分支结构来弥补丢失的信息。为了减小过拟合,采用自行设计的瓶颈层代替模型中的分类层。实验时,分别在Market1501、CUHK03、DukeMTMC-Reid数据集上对本文所提算法进行验证。在Market1501数据集上,本文所提算法预测正确的概率(Rank1)为95.2%,平均预测均值(mAP)为86.0%。实验结果表明,本文所提算法提取的行人特征具有较强的辨别力,识别准确率明显高于其他先进的算法。

A convolutional neural networks based algorithm is proposed to extract multiple features from a single person.Further,the feature representation of a person using spliced multi-features is also proposed.Initially,the multi-branch structure is constructed using global pooling and multiple convolution;this multi-branch structure is used to offset the information loss.Subsequently,the bottleneck layer is designed to replace the classification layer in the model to reduce overfitting.In the experiment,the proposed algorithm is verified using the Market1501,CUHK03,and DukeMTMC-Reid datasets.In Market1501,the proposed algorithm achieves the first correct prediction probability(Rank1)of 95.2% and mean average precision(mAP)of 86.0%.The experimental results indicate that the proposed algorithm can extract discriminative features.Furthermore,the recognition accuracy of the proposed algorithm is significantly better than that of other advanced algorithms.