Interaction-via-Actions: Cattle Interaction Detection with Joint Learning of Action-Interaction Latent Space

• URL: http://arxiv.org/abs/2512.16133v1
• Date: Thu, 18 Dec 2025 03:42:54 GMT
• Title: Interaction-via-Actions: Cattle Interaction Detection with Joint Learning of Action-Interaction Latent Space
• Authors: Ren Nakagawa, Yang Yang, Risa Shinoda, Hiroaki Santo, Kenji Oyama, Fumio Okura, Takenao Ohkawa,
• Abstract summary: This paper introduces a method and application for automatically detecting behavioral interactions between grazing cattle from a single image.<n>We propose CattleAct, a data-efficient method for interaction detection by decomposing interactions into the combinations of actions by individual cattle.<n>On top of the proposed method, we develop a practical working system integrating video and GPS inputs.
• Score: 18.635930702079563
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: This paper introduces a method and application for automatically detecting behavioral interactions between grazing cattle from a single image, which is essential for smart livestock management in the cattle industry, such as for detecting estrus. Although interaction detection for humans has been actively studied, a non-trivial challenge lies in cattle interaction detection, specifically the lack of a comprehensive behavioral dataset that includes interactions, as the interactions of grazing cattle are rare events. We, therefore, propose CattleAct, a data-efficient method for interaction detection by decomposing interactions into the combinations of actions by individual cattle. Specifically, we first learn an action latent space from a large-scale cattle action dataset. Then, we embed rare interactions via the fine-tuning of the pre-trained latent space using contrastive learning, thereby constructing a unified latent space of actions and interactions. On top of the proposed method, we develop a practical working system integrating video and GPS inputs. Experiments on a commercial-scale pasture demonstrate the accurate interaction detection achieved by our method compared to the baselines. Our implementation is available at https://github.com/rakawanegan/CattleAct.

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