Skeleton-Guided Spatial-Temporal Feature Learning for Video-Based Visible-Infrared Person Re-Identification

• URL: http://arxiv.org/abs/2411.11069v1
• Date: Sun, 17 Nov 2024 13:18:05 GMT
• Title: Skeleton-Guided Spatial-Temporal Feature Learning for Video-Based Visible-Infrared Person Re-Identification
• Authors: Wenjia Jiang, Xiaoke Zhu, Jiakang Gao, Di Liao,
• Abstract summary: Video-based visible-infrared person re-identification (VVI-ReID) is challenging due to significant modality feature discrepancies. We propose a novel Skeleton-guided spatial-temporal feAture leaRning (STAR) method for VVI-ReID.
• Score: 2.623742123778503
• License:
• Abstract: Video-based visible-infrared person re-identification (VVI-ReID) is challenging due to significant modality feature discrepancies. Spatial-temporal information in videos is crucial, but the accuracy of spatial-temporal information is often influenced by issues like low quality and occlusions in videos. Existing methods mainly focus on reducing modality differences, but pay limited attention to improving spatial-temporal features, particularly for infrared videos. To address this, we propose a novel Skeleton-guided spatial-Temporal feAture leaRning (STAR) method for VVI-ReID. By using skeleton information, which is robust to issues such as poor image quality and occlusions, STAR improves the accuracy of spatial-temporal features in videos of both modalities. Specifically, STAR employs two levels of skeleton-guided strategies: frame level and sequence level. At the frame level, the robust structured skeleton information is used to refine the visual features of individual frames. At the sequence level, we design a feature aggregation mechanism based on skeleton key points graph, which learns the contribution of different body parts to spatial-temporal features, further enhancing the accuracy of global features. Experiments on benchmark datasets demonstrate that STAR outperforms state-of-the-art methods. Code will be open source soon.

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