期刊
PROCEEDINGS OF THE 27TH ACM INTERNATIONAL CONFERENCE ON MULTIMEDIA (MM'19)
卷 -, 期 -, 页码 57-65出版社
ASSOC COMPUTING MACHINERY
DOI: 10.1145/3343031.3351006
关键词
cross-modality; person re-identification; distribution; fine-grained
资金
- National Natural Science Foundation of China [61876142, 61432014, U1605252, 61772402, 61671339]
- National Key Research and Development Program of China [2016QY01W0200]
- National High-Level Talents Special Support Program of China [CS31117200001]
- Young Elite Scientists Sponsorship Program by CAST [2016QNRC001]
- Young Talent fund of University Association for Science and Technology in Shaanxi, China
- CCF-Tencent Open Research Fund [RAGR20180105]
- Tencent AI Lab Rhino-Bird Focused Research Program [JR201923]
- Xidian University-Intellifusion Joint Innovation Laboratory of Artificial Intelligence
- Fundamental Research Funds for the Central Universities [JB190117]
- Innovation Fund of Xidian University
Person re-identification aims at searching pedestrians across different cameras, which is a key problem in video surveillance. With requirements in night environment, RGB-infrared person re-identification which could be regarded as a cross-modality matching problem, has gained increasing attention in recent years. Aside from cross-modality discrepancy, RGB-infrared person re-identification also suffers from human pose and view point differences. We design a dual-alignment feature embedding method to extract discriminative modality-invariant features. The concept of dual-alignment is two folds: spatial and modality alignments. We adopt the part-level features to extract fine-grained camera-invariant information. We introduce distribution loss function and correlation loss function to align the embedding features across visible and infrared modalities. Finally, we can extract modality-invariant features with robust and rich identity embeddings for cross-modality person re-identification. Experiment confirms that the proposed baseline and improvement achieves competitive results with the state-of-the-art methods on two datasets. For instance, We achieve (57.5+12.6)% rank-1 accuracy and (57.3+11.8)% mAP on the RegDB dataset.
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