Optimizing video analytics inference pipelines: a case study

2025-12-07

AI Summary

To address the high computational overhead, latency, and hardware costs associated with real-time, high-resolution video analysis in precision livestock farming, this paper proposes an end-to-end scalable inference optimization framework. Methodologically, it introduces a multi-level parallelization architecture that replaces CPU-intensive modules with GPU acceleration, incorporates a vectorized clustering algorithm, and implements a memory-efficient post-processing pipeline. The key contribution lies in the deep integration of computation, data, and task parallelism—achieving a balanced trade-off between accuracy and efficiency. Experiments on a real-world farm video dataset demonstrate a 2.0× increase in inference throughput, a 48% reduction in end-to-end latency, negligible accuracy loss (mAP degradation <0.3%), and approximately 40% lower hardware resource requirements. This framework significantly enhances the practicality and deployment cost-effectiveness of large-scale video-based animal behavior analysis systems.

Technology Category

• Computer Vision: Video Understanding & Activity Analysis
• Machine Learning: Hardware-aware ML
• Search and Optimization: Distributed Search

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

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.

Problem

• Optimizing video analytics for livestock monitoring efficiency
• Enhancing computational performance in real-time poultry welfare systems
• Reducing infrastructure demands in large-scale video inference applications

Innovation

• GPU-accelerated code substitution for CPU code
• Multi-level parallelization across pipeline modules
• Vectorized clustering with memory-efficient post-processing