Multi-View People Detection in Large Scenes via Supervised View-Wise Contribution Weighting
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arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866913370481885184 |
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| author | Zhang, Qi Gong, Yunfei Chen, Daijie Chan, Antoni B. Huang, Hui |
| author_facet | Zhang, Qi Gong, Yunfei Chen, Daijie Chan, Antoni B. Huang, Hui |
| contents | Recent deep learning-based multi-view people detection (MVD) methods have shown promising results on existing datasets. However, current methods are mainly trained and evaluated on small, single scenes with a limited number of multi-view frames and fixed camera views. As a result, these methods may not be practical for detecting people in larger, more complex scenes with severe occlusions and camera calibration errors. This paper focuses on improving multi-view people detection by developing a supervised view-wise contribution weighting approach that better fuses multi-camera information under large scenes. Besides, a large synthetic dataset is adopted to enhance the model's generalization ability and enable more practical evaluation and comparison. The model's performance on new testing scenes is further improved with a simple domain adaptation technique. Experimental results demonstrate the effectiveness of our approach in achieving promising cross-scene multi-view people detection performance. See code here: https://vcc.tech/research/2024/MVD. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_19943 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Multi-View People Detection in Large Scenes via Supervised View-Wise Contribution Weighting Zhang, Qi Gong, Yunfei Chen, Daijie Chan, Antoni B. Huang, Hui Computer Vision and Pattern Recognition Recent deep learning-based multi-view people detection (MVD) methods have shown promising results on existing datasets. However, current methods are mainly trained and evaluated on small, single scenes with a limited number of multi-view frames and fixed camera views. As a result, these methods may not be practical for detecting people in larger, more complex scenes with severe occlusions and camera calibration errors. This paper focuses on improving multi-view people detection by developing a supervised view-wise contribution weighting approach that better fuses multi-camera information under large scenes. Besides, a large synthetic dataset is adopted to enhance the model's generalization ability and enable more practical evaluation and comparison. The model's performance on new testing scenes is further improved with a simple domain adaptation technique. Experimental results demonstrate the effectiveness of our approach in achieving promising cross-scene multi-view people detection performance. See code here: https://vcc.tech/research/2024/MVD. |
| title | Multi-View People Detection in Large Scenes via Supervised View-Wise Contribution Weighting |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2405.19943 |