Skeleton-Guided Spatial-Temporal Feature Learning for Video-Based Visible-Infrared Person Re-Identification
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866929623739138048 |
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| author | Jiang, Wenjia Zhu, Xiaoke Gao, Jiakang Liao, Di |
| author_facet | Jiang, Wenjia Zhu, Xiaoke Gao, Jiakang Liao, Di |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2411_11069 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Skeleton-Guided Spatial-Temporal Feature Learning for Video-Based Visible-Infrared Person Re-Identification Jiang, Wenjia Zhu, Xiaoke Gao, Jiakang Liao, Di Computer Vision and Pattern Recognition 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. |
| title | Skeleton-Guided Spatial-Temporal Feature Learning for Video-Based Visible-Infrared Person Re-Identification |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2411.11069 |