Human Pose Estimation for Real-World Deployment: A Review of Methods, Systems, and Applications

Human pose estimation (HPE) has become a core technology for human-centered visual understanding and is widely used in sports analysis, rehabilitation assessment, human–computer interaction, autonomous driving, and industrial safety monitoring. Although benchmark-driven research has substantially improved pose estimation accuracy, strong performance on standard datasets does not necessarily ensure reliable deployment in real-world environments. Practical HPE systems are commonly deployed in camera-based, mobile, wearable, and edge-computing environments. In these settings, sensor quality, device placement, frame rate, illumination conditions, hardware limitations, temporal instability, and application-specific reliability requirements jointly influence system performance. Using a structured narrative synthesis of representative literature, this review examines HPE from a deployment-oriented perspective instead of treating pose estimation solely as a model-level task. The review first summarizes the major benchmark-to-deployment gaps, including data, system, task, and temporal gaps; it then analyzes the end-to-end HPE pipeline, including image or video acquisition, human detection, region localization, pose inference, tracking, temporal smoothing, coordinate decoding, and task-level decision-making. Representative CNN-based, Transformer-based, temporal modeling, and lightweight optimization methods are reviewed in terms of deployment cost, optimization potential, and application suitability. This review also discusses practical application requirements and future research directions related to deployment-aware evaluation, system-level optimization, efficient temporal modeling, and application-level reliability. By linking benchmark performance with practical sensing and system constraints, this review provides a structured reference for the selection, evaluation, and deployment of HPE methods in real-world environments.