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.

翻译：经济高效且可扩展的视频分析对于精准畜牧监测至关重要，商业农场产生的高分辨率视频素材和近实时监测需求带来了巨大的计算负载。本文通过系统级改进，对家禽福利监测系统的检测、跟踪、聚类和行为分析模块进行了全面的案例研究。我们提出了一系列优化方法，包括多级并行化、通过用GPU加速代码替代CPU代码进行代码优化、向量化聚类以及内存高效的后处理。在真实农场视频素材上的评估表明，这些改进在不降低模型精度的前提下，使流水线整体实现了最高2倍的加速。我们的研究结果强调了构建高吞吐、低延迟视频推理系统的实用策略，这些策略能够降低农业与智能传感部署以及其他大规模视频分析应用中的基础设施需求。