LAPX: Lightweight Hourglass Network with Global Context
This work addresses the need for efficient and accurate human pose estimation models suitable for deployment on edge devices, representing an incremental improvement over existing lightweight methods.
The paper tackles the problem of balancing accuracy and efficiency in human pose estimation for edge devices by proposing LAPX, a lightweight hourglass network with global context, which achieves competitive results on MPII and COCO benchmarks with 2.3M parameters and real-time performance.
Human pose estimation is a crucial task in computer vision. Methods that have SOTA (State-of-the-Art) accuracy, often involve a large number of parameters and incur substantial computational cost. Many lightweight variants have been proposed to reduce the model size and computational cost of them. However, several of these methods still contain components that are not well suited for efficient deployment on edge devices. Moreover, models that primarily emphasize inference speed on edge devices often suffer from limited accuracy due to their overly simplified designs. To address these limitations, we propose LAPX, an Hourglass network with self-attention that captures global contextual information, based on previous work, LAP. In addition to adopting the self-attention module, LAPX advances the stage design and refine the lightweight attention modules. It achieves competitive results on two benchmark datasets, MPII and COCO, with only 2.3M parameters, and demonstrates real-time performance, confirming its edge-device suitability.