Optimizing video analytics inference pipelines: a case study

TL;DR

This paper presents system-level optimizations for poultry welfare video analytics, achieving up to 2x speedup in inference pipelines while maintaining accuracy, thereby enabling scalable and cost-effective livestock monitoring.

Contribution

It introduces a set of practical, system-level optimizations including parallelization, GPU acceleration, and memory-efficient processing for large-scale video analytics.

Findings

Up to 2x speedup in inference pipelines

Maintained model accuracy after optimizations

Reduced infrastructure demands for large-scale deployment

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.