基于全局特征拼接的行人重识别算法研究.

In order to solve the problems of network model complexity and low identification rate, this paper proposed a person re-identification( Re!D) method based on global feature stitching. Firstly, it extracted the global features using convolutional neural network (CNN). Secondly, it stitched the features from different convolution layers together to complement the feature information. Finally, it convoluted again to obtain the features with high representation ability. In network training stage, it combined the cluster loss with label smoothing loss, and adopted random erasing augmentation (REA) as well as pooling step reduction techniques. It conducted extensive experiments to evaluate the performance of this proposed method on Marketl501, DukeMTMC-re!D, CUHK03 and MSMTl 7 benchmark datasets. Results show that the proposed method outperforms other state-of-the-art techniques, for instance, the Rank-1 and mAP on Marketl501 are 95. 9% and 94. 6%, respectively. [ABSTRACT FROM AUTHOR]