{"ID":2889738,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.21021","arxiv_id":"2507.21021","title":"Behavior-Specific Filtering for Enhanced Pig Behavior Classification in Precision Livestock Farming","abstract":"This study proposes a behavior-specific filtering method to improve behavior classification accuracy in Precision Livestock Farming. While traditional filtering methods, such as wavelet denoising, achieved an accuracy of 91.58%, they apply uniform processing to all behaviors. In contrast, the proposed behavior-specific filtering method combines Wavelet Denoising with a Low Pass Filter, tailored to active and inactive pig behaviors, and achieved a peak accuracy of 94.73%. These results highlight the effectiveness of behavior-specific filtering in enhancing animal behavior monitoring, supporting better health management and farm efficiency.","short_abstract":"This study proposes a behavior-specific filtering method to improve behavior classification accuracy in Precision Livestock Farming. While traditional filtering methods, such as wavelet denoising, achieved an accuracy of 91.58%, they apply uniform processing to all behaviors. In contrast, the proposed behavior-specific...","url_abs":"https://arxiv.org/abs/2507.21021","url_pdf":"https://arxiv.org/pdf/2507.21021v1","authors":"[\"Zhen Zhang\",\"Dong Sam Ha\",\"Gota Morota\",\"Sook Shin\"]","published":"2025-07-28T17:42:57Z","proceeding":"cs.LG","tasks":"[\"cs.LG\"]","methods":"[]","has_code":false}