BK-POSE: A LIGHTWEIGHT MODEL FOR MULTI-PERSON POSE ESTIMATION IN THE WILD

Tóm tắt

Human pose estimation is an essential topic in computer vision research, which has numerous applications in various fields. In this article, we propose a lightweight deep learning architecture called BK-Pose for recognizing human keypoints from images and videos that can be executed on edge computing devices. The main contributions of our work are two folds: (1) A lightweight bottom-up deep learning model is introduced that can be deployed on edge devices and can achieve high frames-per-second (FPS) rates while maintaining acceptable accuracy in suitable environments. (2) The incorporation of a novel focal lL2 loss (lFlL) technique allows for the effective balancing between "hard" and "easy" keypoints samples during training.

The performance of the BK-Pose model is evaluated within a classroom environment for capturing students' keypoints and demonstrate its efficacy. Our results show promise for further research in activity recognition using the proposed architecture.