Dual-alignment Feature Embedding for Cross-modality Person Re-identification

Abstract

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.