Optimizing video analytics inference pipelines: a case study
This provides practical cost-saving solutions for agricultural and smart sensing deployments, though it appears incremental as it applies known optimization techniques to a specific domain.
This paper tackled the challenge of computational efficiency in video analytics for precision livestock monitoring by implementing system-level optimizations across multiple pipeline modules, achieving up to 2x speedup on real-world farm footage without accuracy loss.
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.