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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2512.07009 |
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| _version_ | 1866912753480892416 |
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| author | Ghafouri, Saeid Ding, Yuming Chito, Katerine Diaz del Rincón, Jesús Martinez O'Connell, Niamh Vandierendonck, Hans |
| author_facet | Ghafouri, Saeid Ding, Yuming Chito, Katerine Diaz del Rincón, Jesús Martinez O'Connell, Niamh Vandierendonck, Hans |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_07009 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Optimizing video analytics inference pipelines: a case study Ghafouri, Saeid Ding, Yuming Chito, Katerine Diaz del Rincón, Jesús Martinez O'Connell, Niamh Vandierendonck, Hans Distributed, Parallel, and Cluster Computing Artificial Intelligence Machine Learning 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. |
| title | Optimizing video analytics inference pipelines: a case study |
| topic | Distributed, Parallel, and Cluster Computing Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2512.07009 |