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.
@article{arxiv.2512.07009,
title = {Optimizing video analytics inference pipelines: a case study},
author = {Saeid Ghafouri and Yuming Ding and Katerine Diaz Chito and Jesús Martinez del Rincón and Niamh O'Connell and Hans Vandierendonck},
journal= {arXiv preprint arXiv:2512.07009},
year = {2025}
}
Comments
Accepted to the IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT 2025)