Progressive unsupervised video person re-identification with accumulative motion and tracklet spatial-temporal correlation

卷:142

Article

关键词: GATHERING PATTERNS;    DISCOVERY;    ATTENTION;   

DOI  :  10.1016/j.future.2022.12.023

来源: SCIE

【 摘 要 】

Person re-identification (re-id) methods based on supervised learning require a large number of manually labeled samples for training, resulting in poor scalability in actual re-id applications. Existing unsupervised video person re-id methods typically focus on extracting appearance features from pedestrian videos, ignoring motion information and the fact that people usually move in groups, i.e., pedestrian spatio-temporal co-occurrence patterns. The key factor for unsupervised video person re-id is to effectively exploit both spatio-temporal clues from video sequences and cross-camera tracklet association. In this work, we propose a progressive deep learning method for unsupervised person re-id via tracklet association with spatio-temporal correlation (TASTC). Specifically, we first divide uniformly each tracklet into multiple temporally localized slices according to a time pyramid structure. Then, an initial re-id model is trained based on a two-stream convolutional architecture, and the accumulative motion context information of temporally localized slices of the tracklets per camera is learned. Finally, combining accumulative motion and tracklet spatial-temporal correlation, we associate tracklets across cameras and update the re-id model. The above steps are iterated to optimize the re-id model progressively. Experiment results demonstrate that the proposed method significantly outperforms the current state-of-the-art unsupervised video person re-identification methods on three video-based benchmark datasets, ILIDS-VID, MARS, and DukeMTMC-VideoReID.(c) 2022 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

【 授权许可】

Free