优化禽类福利监测系统，实现2倍提速且不降精度。
Optimizing video analytics inference pipelines: a case study
- 多级并行+GPU加速+向量化聚类，系统级优化视频分析流程。
- 真实农场数据测试，推理速度最高提升2倍，准确率不变。
- 适合农业监控、智能传感等大规模视频分析场景部署。
低成本且可扩展的视频分析对精准畜牧监测至关重要。商业农场需高分辨率画面与近实时监控，带来巨大计算负载。本文通过检测、跟踪、聚类和行为分析模块的系统级优化，开展全面案例研究。引入多级并行、用GPU加速代码替代CPU代码、向量化聚类及内存高效的后处理技术。在真实农场视频数据上评估，这些优化使整体流水线速度最高提升2倍，同时保持模型精度。结果表明，该策略可构建高吞吐、低延迟的视频推理系统，降低农业与智能感知部署中的基础设施需求，适用于其他大规模视频分析应用。
原文摘要 · Abstract (English)
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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