Gait Anomaly Detection in Dairy Cattle via Motion Reconstruction with Subspace Representation Learning

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Other Title
  • 部分空間学習に基づくモーション再構成による乳牛の軽度跛行検知
  • ブブン クウカン ガクシュウ ニ モトズク モーション サイコウセイ ニ ヨル ニュウギュウ ノ ケイドハコウケンチ
Published
2026
DOI
  • 10.9746/sicetr.62.12
Publisher
The Society of Instrument and Control Engineers

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<p>Detecting lameness in dairy cattle is essential to mitigating the effects of a significant animal welfare and health issue for the dairy industry across diverse farming systems. The locomotion score, a standard method for lameness evaluation, depends on visual assessments conducted by experienced and skilled observers, which limits the objectivity of diagnoses on large-scale farms. In this paper, we propose a gait anomaly detection method through motion reconstruction, which is based on identifying low-dimensional subspaces derived from normal motion patterns within the training data. Specifically, we first generate smooth motions from the trajectories of each key point extracted by a skeleton extraction network in a video. We then use a self-expressive model to learn subspaces from normal motions and reconstruct the given test motion. Our reconstruction module leverages the insight that as the subspace-based approximation strategy only enables reproducing the normal motions, the anomalous motions would induce a significant reconstruction error. Experimental results using cattle gait dataset demonstrate the effectiveness of the proposed method through quantitative and qualitative evaluation.</p>

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