{"ID":2863335,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.24329","arxiv_id":"2509.24329","title":"TP-MVCC: Tri-plane Multi-view Fusion Model for Silkie Chicken Counting","abstract":"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.","short_abstract":"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 mult...","url_abs":"https://arxiv.org/abs/2509.24329","url_pdf":"https://arxiv.org/pdf/2509.24329v1","authors":"[\"Sirui Chen\",\"Yuhong Feng\",\"Yifeng Wang\",\"Jianghai Liao\",\"Qi Zhang\"]","published":"2025-09-29T06:27:04Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[]","has_code":false}