Cattle-CLIP: A Multimodal Framework for Cattle Behaviour Recognition from Video
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866913008670736384 |
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| author | Liu, Huimin Gao, Jing Baran, Daria Montout, AxelX Campbell, Neill W Dowsey, Andrew W |
| author_facet | Liu, Huimin Gao, Jing Baran, Daria Montout, AxelX Campbell, Neill W Dowsey, Andrew W |
| contents | Robust behaviour recognition in real-world farm environments remains challenging due to several data-related limitations, including the scarcity of well-annotated livestock video datasets and the substantial domain gap between large-scale pre-training corpora and agricultural surveillance footage. To address these challenges, we propose Cattle-CLIP, a domain-adaptive vision-language framework that reformulates cattle behaviour recognition as cross-modal semantic alignment rather than purely visual classification. Instead of directly fine-tuning visual backbones, Cattle-CLIP incorporates a temporal integration module to extend image-level contrastive pre-training to video-based behaviour understanding, enabling consistent semantic alignment across time. To mitigate the distribution shift between web-scale image-text data used for the pre-trained model and real-world cattle surveillance footage, we further introduce tailored augmentation strategies and specialised behaviour prompts. Furthermore, we construct CattleBehaviours6, a curated and behaviour-consistent video dataset comprising 1905 annotated clips across six indoor behaviours to support model training and evaluation. Beyond serving as a benchmark for our proposed method, the dataset provides a standardised ethogram definition, offering a practical resource for future research in livestock behaviour analysis. Cattle-CLIP is evaluated under both fully-supervised and few-shot learning scenarios, with a particular focus on data-scarce behaviour recognition, an important yet under-explored goal in livestock monitoring. Experiments show that Cattle-CLIP achieves 96.1% overall accuracy across six behaviours in supervised settings, with near-perfect recall for feeding, drinking and standing-ruminating behaviours, and demonstrates robust generalisation with limited data in few-shot scenarios. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_09203 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Cattle-CLIP: A Multimodal Framework for Cattle Behaviour Recognition from Video Liu, Huimin Gao, Jing Baran, Daria Montout, AxelX Campbell, Neill W Dowsey, Andrew W Computer Vision and Pattern Recognition Robust behaviour recognition in real-world farm environments remains challenging due to several data-related limitations, including the scarcity of well-annotated livestock video datasets and the substantial domain gap between large-scale pre-training corpora and agricultural surveillance footage. To address these challenges, we propose Cattle-CLIP, a domain-adaptive vision-language framework that reformulates cattle behaviour recognition as cross-modal semantic alignment rather than purely visual classification. Instead of directly fine-tuning visual backbones, Cattle-CLIP incorporates a temporal integration module to extend image-level contrastive pre-training to video-based behaviour understanding, enabling consistent semantic alignment across time. To mitigate the distribution shift between web-scale image-text data used for the pre-trained model and real-world cattle surveillance footage, we further introduce tailored augmentation strategies and specialised behaviour prompts. Furthermore, we construct CattleBehaviours6, a curated and behaviour-consistent video dataset comprising 1905 annotated clips across six indoor behaviours to support model training and evaluation. Beyond serving as a benchmark for our proposed method, the dataset provides a standardised ethogram definition, offering a practical resource for future research in livestock behaviour analysis. Cattle-CLIP is evaluated under both fully-supervised and few-shot learning scenarios, with a particular focus on data-scarce behaviour recognition, an important yet under-explored goal in livestock monitoring. Experiments show that Cattle-CLIP achieves 96.1% overall accuracy across six behaviours in supervised settings, with near-perfect recall for feeding, drinking and standing-ruminating behaviours, and demonstrates robust generalisation with limited data in few-shot scenarios. |
| title | Cattle-CLIP: A Multimodal Framework for Cattle Behaviour Recognition from Video |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2510.09203 |