Aiming at the problem that the existing pedestrian recognition technology re-identification effect is not good and the traditional method has low recognition effect. A feature fusion network is proposed in this paper,...Aiming at the problem that the existing pedestrian recognition technology re-identification effect is not good and the traditional method has low recognition effect. A feature fusion network is proposed in this paper, which combines the CNN features extracted by ResNet with the manual annotation attributes into a unified feature space. ResNet solved the problem of network degradation and multi-convergence in multi-layer CNN training, and extracted deeper features. The attribute combination method was adopted by the artificial annotation attributes. The CNN features were constrained by the hand-crafted features because of the back propagation. Then the loss measurement function was used to optimize network identification results. In the public datasets VIPeR, PRID, and CUHK for further testing, the experimental results show that the method achieves a high cumulative matching score.展开更多
文摘Aiming at the problem that the existing pedestrian recognition technology re-identification effect is not good and the traditional method has low recognition effect. A feature fusion network is proposed in this paper, which combines the CNN features extracted by ResNet with the manual annotation attributes into a unified feature space. ResNet solved the problem of network degradation and multi-convergence in multi-layer CNN training, and extracted deeper features. The attribute combination method was adopted by the artificial annotation attributes. The CNN features were constrained by the hand-crafted features because of the back propagation. Then the loss measurement function was used to optimize network identification results. In the public datasets VIPeR, PRID, and CUHK for further testing, the experimental results show that the method achieves a high cumulative matching score.
