Spatial-wise and channel-wise feature uncertainty for occluded person re-identification

Occluded person re-identification is a challenging task since the available data often suffers from information incompleteness and spatial misalignment. Most state-of-the-art occluded models rely on the external model to provide additional semantic information. However, for the time being, external models, such as the human parsing model and the pose estimation model cannot provide accurate semantic information under a complex occlusion environment and may introduce errors to the Re-ID model instead. In this paper, we propose an occluded person Re-ID model that mines the latent recognizable information of the person image itself, without the help of external models. Feature/Data uncertainty can reduce the influence of noisy samples in datasets and has been discussed in person Re-ID and face recognition, we extend the uncertainty to the micro feature level, and propose the spatial-wise and channel-wise feature uncertainty to constantly refine the features in the spatial domain and the channel domain respectively during feature construction by weakening the influence of noise features. Extensive experiments on the occluded datasets and holistic datasets have proved the effectiveness of our proposed methods. (c) 2021 Elsevier B.V. All rights reserved.