Robust Monocular Human Height Estimation via a Temporal SegPose Framework and Three-Way Orthogonal Playground Calibration

Accurate non-contact human height estimation is vital for large-scale growth monitoring in schools but remains challenging for monocular RGB sensors due to scale ambiguity and keypoint jitter. This study proposes a robust temporal SegPose framework for high-precision height measurement in unconstrained outdoor playground environments. We develop a multi-task deep learning model using a MobileNetV4 backbone and a novel Height-Aware Boundary Refinement (HABR) module, which utilizes nose-spatial priors to refine cranial vertex localization. To resolve scale issues, a three-way orthogonal calibration system is established using existing playground marking lines and goalposts to dynamically estimate ground plane metric factors. A linear Kalman filter is integrated to smooth keypoint trajectories, suppressing gait-induced oscillations and reducing high-frequency jitter by 64.92%. Validated on a dataset of 95 volunteers (53 males, 42 females) at distances of 6–12 m, the proposed system achieves a mean absolute error (MAE) of 1.42 cm and a mean absolute percentage error (MAPE) of 0.84%, significantly outperforming recent Transformer-based state-of-the-art methods. The framework operates at 42.7 FPS, ensuring real-time performance while adhering to a privacy-preserving protocol that decouples biometric records from individual identities. These results demonstrate that our framework effectively overcomes boundary ambiguity and distance-dependent resolution loss, providing a reliable, efficient, and ethical solution for automated physical health assessments in educational settings.