Moderate Clothes-Changing Person Re-identification Based on Bionics of Binocular Summation

Moderate clothes-changing person re-identification is to find the same person from different camera scenes under the premise of considering the moderate change of clothes.The implementation of existing person re-identification methods is usually based on the assumption that the pedestrian’s clothing is invariant,so they rely on clothing-related features.Then,when the above assumptions are not valid,these methods are difficult to achieve the ideal recognition performance.Considering the important characteristic that pedestrian’s shape hardly change when the change of clothes is moderate,the moderate clothes-changing person re-identification is studied.Inspired by the binocular summation in biological vision system,a self-attention siamese network is proposed in this paper.Analogous to biological binocular information acquisition process,the network takes two types of images of the same pedestrian with different clothes as two branch inputs,and then achieves summation effect with siamese architecture.Subsequently,the contrastive learning and fusion learning of multiple features are carried out to obtain the pedestrian feature representation with identity discrimination.Finally,empirical studies show that the proposed method achieves best performance at present on clothes-changing person re-identification benchmark.