Camera viewpoint learning for person reidentification

Abstract

Camera viewpoint has a great influence on reidentification (re-ID) for its diversity. However, there are few methods in response to it, which limits the development of the re-ID community. We analyze the influence of camera viewpoint on re-ID to obtain both visualization and quantitative results, indicating the potential to solve this problem. On this basis, we propose mean and variance loss function (MVL) and camera ID classifier (CIC). MVL reduces the gap among the mean and variance of the feature vectors of images acquired by different cameras, so that the distribution of image feature vectors under different cameras is similar. CIC filters out the images, which have the same camera id with the query to avoid recognition interference. The experiments are conducted on Market1501 and DukeMTMC-reID datasets. We show that MVL and CIC can mitigate the negative impact of the camera viewpoint for re-ID and further improve the accuracy of re-ID. In the Market1501 dataset, rank-1 [mean average precision score (mAP)] accuracy is improved from 88.1 (71.1) % to 90.0 (73.9) % for ResNet-50. In the DukeMTMC-reID dataset, rank-1 (mAP) accuracy is improved from 77.0 (58.9) % to 83.9 (64.1) % for ResNet-50.