基于VGG-NET的行人重识别

Person Re-identification Based on VGG-NET

首先采用卷积神经网络中的ALEX-NET和VGG-NET网络在Market-1501数据集上对行人特征进行提取,然后将提取出的行人特征进行相似性度量,通过计算行人准确率mAP值来实现最优匹配。实验证明,VGGNET模型较ALEX-NET模型的特征提取效果好,行人重识别的准确率高。

In this paper,firstly the ALEX-NET and VGG-NET of convolutional neural network are used to extract pedestrian features on the Market-1501 data set.Then,the extracted pedestrian features are measured for similarity,and the optimal matching is realized by calculating the mAP value of pedestrian accuracy.Experiment shows that VGG-NET model performs better than ALEX-NET model in feature extraction,and the mAP value of pedestrian recognition is higher.