Pedestrian Re-IDentification Algorithm Based on Dual-domain Filtering and Triple Metric Learning

Noise may be generated in the process of image capture, transmission or processing. When the image is affected by a large amount of noise, it is difficult for many pedestrian Re-IDentification(ReID) methods to extract pedestrian features with sufficient expressive ability, which shows poor robustness. This paper focuses on the pedestrian re-identification with low quality image. The dual-domain filtering decomposition is proposed to construct triplet, which is used to train metric learning model. The proposed method mainly consists of two parts. Firstly, the distribution characteristics of different image noise in surveillance videos is analyzed and images are enhanced by dual-domain filtering. Secondly, based on the separation effect of dual-domain filtering, a new triplet is proposed. In the training stage, the original image with the low-frequency component, the noise with high-frequency component generated by the dual-domain filtering and the original image are used as the input triplet. So the noise component can be further suppressed by the network. At the same time, the loss function is optimized, and the triple loss and contrast loss are used in combination. Finally, re-ranking is used to expand the sorting table to improve the accuracy of identification. The average Rank-1 on the noisy Market1501 and CUHK03 datasets are 78.3% and 21.7%, and the mean Average Precision(mAP) is 66.9% and 20.5%. The accuracy loss of Rank-1 before and after adding noise is only 1.9% and 7.8%, which indicates that the model in this paper shows strong robustness in the case of noise.