TP-MVCC: Tri-plane Multi-view Fusion Model for Silkie Chicken Counting
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866916976056598528 |
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| author | Chen, Sirui Feng, Yuhong Wang, Yifeng Liao, Jianghai Zhang, Qi |
| author_facet | Chen, Sirui Feng, Yuhong Wang, Yifeng Liao, Jianghai Zhang, Qi |
| contents | Accurate animal counting is essential for smart farming but remains difficult in crowded scenes due to occlusions and limited camera views. To address this, we propose a tri-plane-based multi-view chicken counting model (TP-MVCC), which leverages geometric projection and tri-plane fusion to integrate features from multiple cameras onto a unified ground plane. The framework extracts single-view features, aligns them via spatial transformation, and decodes a scene-level density map for precise chicken counting. In addition, we construct the first multi-view dataset of silkie chickens under real farming conditions. Experiments show that TP-MVCC significantly outperforms single-view and conventional fusion comparisons, achieving 95.1\% accuracy and strong robustness in dense, occluded scenarios, demonstrating its practical potential for intelligent agriculture. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_24329 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | TP-MVCC: Tri-plane Multi-view Fusion Model for Silkie Chicken Counting Chen, Sirui Feng, Yuhong Wang, Yifeng Liao, Jianghai Zhang, Qi Computer Vision and Pattern Recognition Accurate animal counting is essential for smart farming but remains difficult in crowded scenes due to occlusions and limited camera views. To address this, we propose a tri-plane-based multi-view chicken counting model (TP-MVCC), which leverages geometric projection and tri-plane fusion to integrate features from multiple cameras onto a unified ground plane. The framework extracts single-view features, aligns them via spatial transformation, and decodes a scene-level density map for precise chicken counting. In addition, we construct the first multi-view dataset of silkie chickens under real farming conditions. Experiments show that TP-MVCC significantly outperforms single-view and conventional fusion comparisons, achieving 95.1\% accuracy and strong robustness in dense, occluded scenarios, demonstrating its practical potential for intelligent agriculture. |
| title | TP-MVCC: Tri-plane Multi-view Fusion Model for Silkie Chicken Counting |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2509.24329 |