LiCAF: LiDAR-Camera Asymmetric Fusion for Gait Recognition
Fuente:
arXiv
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866913394515247104 |
|---|---|
| author | Deng, Yunze Xiong, Haijun Feng, Bin |
| author_facet | Deng, Yunze Xiong, Haijun Feng, Bin |
| contents | Gait recognition is a biometric technology that identifies individuals by using walking patterns. Due to the significant achievements of multimodal fusion in gait recognition, we consider employing LiDAR-camera fusion to obtain robust gait representations. However, existing methods often overlook intrinsic characteristics of modalities, and lack fine-grained fusion and temporal modeling. In this paper, we introduce a novel modality-sensitive network LiCAF for LiDAR-camera fusion, which employs an asymmetric modeling strategy. Specifically, we propose Asymmetric Cross-modal Channel Attention (ACCA) and Interlaced Cross-modal Temporal Modeling (ICTM) for cross-modal valuable channel information selection and powerful temporal modeling. Our method achieves state-of-the-art performance (93.9% in Rank-1 and 98.8% in Rank-5) on the SUSTech1K dataset, demonstrating its effectiveness. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_12355 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | LiCAF: LiDAR-Camera Asymmetric Fusion for Gait Recognition Deng, Yunze Xiong, Haijun Feng, Bin Computer Vision and Pattern Recognition Gait recognition is a biometric technology that identifies individuals by using walking patterns. Due to the significant achievements of multimodal fusion in gait recognition, we consider employing LiDAR-camera fusion to obtain robust gait representations. However, existing methods often overlook intrinsic characteristics of modalities, and lack fine-grained fusion and temporal modeling. In this paper, we introduce a novel modality-sensitive network LiCAF for LiDAR-camera fusion, which employs an asymmetric modeling strategy. Specifically, we propose Asymmetric Cross-modal Channel Attention (ACCA) and Interlaced Cross-modal Temporal Modeling (ICTM) for cross-modal valuable channel information selection and powerful temporal modeling. Our method achieves state-of-the-art performance (93.9% in Rank-1 and 98.8% in Rank-5) on the SUSTech1K dataset, demonstrating its effectiveness. |
| title | LiCAF: LiDAR-Camera Asymmetric Fusion for Gait Recognition |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2406.12355 |