Pseudo-positive regularization for deep person re-identification

An intrinsic challenge of person re-identification (re-ID) is the annotation difficulty. This typically means (1) few training samples per identity and (2) thus the lack of diversity among the training samples. Consequently, we face high risk of over-fitting when training the convolutional neural network (CNN), a state-of-the-art method in person re-ID. To reduce the risk of over-fitting, this paper proposes a Pseudo-Positive Regularization method to enrich the diversity of the training data. Specifically, unlabeled data from an independent pedestrian database are retrieved using the target training data as query. A small proportion of these retrieved samples are randomly selected as the Pseudo-Positive samples and added to the target training set for the supervised CNN training. The addition of Pseudo-Positive samples is therefore a Data Augmentation method to reduce the risk of over-fitting during CNN training. We implement our idea in the identification CNN models (i.e., CaffeNet, VGGNet-16 and ResNet-50). On CUHK03 and Market-1501 datasets, experimental results demonstrate that the proposed method consistently improves the baseline and yields competitive performance to the state-of-the-art person re-ID methods.