Abstract
Person re-identification is an important topic in intelligent surveillance and computer vision. It aims to accurately
measure visual similarities between person images for determining whether two images correspond to the same person. State-of-the-art methods mainly utilize deep learning
based approaches for learning visual features for describing person appearances. However, we observe that existing deep learning models are biased to capture too much
relevance between background appearances of person images. We design a series of experiments with newly created datasets to validate the influence of background information. To solve the background bias problem, we propose a person-region guided pooling deep neural network
based on human parsing maps to learn more discriminative
person-part features, and propose to augment training data
with person images with random background. Extensive experiments demonstrate the robustness and effectiveness of
our proposed method