PGAN: Part-Based Nondirect Coupling Embedded GAN for Person Reidentification
Yue Zhang, Yi Jin, Jianqiang Chen, Shichao Kan, Yigang Cen, Qi Cao
Abstract
The block-based representation learning method has been proven to be a very effective method for person reidentification (Re-ID), but the features extracted by the existing block-based approach tend to have a high correlation among different blocks. Also, these methods perform less well for persons with large posture changes. Thus, part-based nondirect coupling representation learning method is proposed by introducing a similarity measure loss to constrain features of different blocks. Moreover, part-based nondirect coupling embedded GAN method is proposed, which aims to extract more common features of different postures of a same person. In this way, the extracted features of the network are robust for posture changes of a person, and there are no auxiliary pose information and additional computational cost required in the test stage. Experimental results on public datasets show that our proposed method achieves good performances, especially, it outperforms the state-of-the-art GAN-based methods for person Re-ID.