{"ID":2832121,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.07009","arxiv_id":"2512.07009","title":"Optimizing video analytics inference pipelines: a case study","abstract":"Cost-effective and scalable video analytics are essential for precision livestock monitoring, where high-resolution footage and near-real-time monitoring needs from commercial farms generates substantial computational workloads. This paper presents a comprehensive case study on optimizing a poultry welfare monitoring system through system-level improvements across detection, tracking, clustering, and behavioral analysis modules. We introduce a set of optimizations, including multi-level parallelization, Optimizing code with substituting CPU code with GPU-accelerated code, vectorized clustering, and memory-efficient post-processing. Evaluated on real-world farm video footage, these changes deliver up to a 2x speedup across pipelines without compromising model accuracy. Our findings highlight practical strategies for building high-throughput, low-latency video inference systems that reduce infrastructure demands in agricultural and smart sensing deployments as well as other large-scale video analytics applications.","short_abstract":"Cost-effective and scalable video analytics are essential for precision livestock monitoring, where high-resolution footage and near-real-time monitoring needs from commercial farms generates substantial computational workloads. This paper presents a comprehensive case study on optimizing a poultry welfare monitoring s...","url_abs":"https://arxiv.org/abs/2512.07009","url_pdf":"https://arxiv.org/pdf/2512.07009v1","authors":"[\"Saeid Ghafouri\",\"Yuming Ding\",\"Katerine Diaz Chito\",\"Jesús Martinez del Rincón\",\"Niamh O'Connell\",\"Hans Vandierendonck\"]","published":"2025-12-07T21:17:53Z","proceeding":"cs.DC","tasks":"[\"cs.DC\",\"cs.AI\",\"cs.LG\"]","methods":"[]","has_code":false}