Research on Person Pose Estimation Based on Parameter Inverted Pyramid and High-Dimensional Feature Enhancement

Abstract

Heating, Ventilation and Air Conditioning (HVAC) systems are significant carbon emitters in buildings, and precise regulation is crucial for achieving carbon neutrality. Computer vision-based occupant behavior prediction provides vital data for demand-driven control strategies. Real-time multi-person pose estimation faces challenges in balancing speed and accuracy, especially in complex environments. Traditional top-down methods become computationally expensive as the number of people increases, while bottom-up methods struggle with key point mismatches in dense crowds. This paper introduces the Efficient-RTMO model, which leverages the Parameter Inverted Image Pyramid (PIIP) with hierarchical multi-scale symmetry for lightweight processing of high-resolution images and a deeper network for low-resolution images. This approach reduces computational complexity, particularly in dense crowd scenarios, and incorporates a dynamic sparse connectivity mechanism via the star-shaped dynamic feed-forward network (StarFFN). By optimizing the symmetry structure, it improves inference efficiency and ensures effective feature fusion. Experimental results on the COCO dataset show that Efficient-RTMO outperforms the baseline RTMO model, achieving more than 2× speed improvement and a 0.3 AP increase. Ablation studies confirm that PIIP and StarFFN enhance robustness against occlusions and scale variations, demonstrating their synergistic effectiveness.