Optimizing video analytics inference pipelines: a case study

• URL: http://arxiv.org/abs/2512.07009v1
• Date: Sun, 07 Dec 2025 21:17:53 GMT
• Title: Optimizing video analytics inference pipelines: a case study
• Authors: Saeid Ghafouri, Yuming Ding, Katerine Diaz Chito, Jesús Martinez del Rincón, Niamh O'Connell, Hans Vandierendonck,
• Abstract summary: This paper presents a comprehensive case study on optimizing a poultry welfare monitoring system.<n>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.
• Score: 3.4152678224558333
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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