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