Advancements in Repetitive Action Counting: Joint-Based PoseRAC Model With Improved Performance
This work improves repetitive counting for applications such as fitness tracking and rehabilitation, representing an incremental advancement over prior methods.
The paper tackled repetitive action counting by integrating joint angles with body pose landmarks to address issues like viewpoint changes and over-counting, achieving a Mean Absolute Error of 0.211 and an Off-By-One accuracy of 0.599 on the RepCount dataset.
Repetitive counting (RepCount) is critical in various applications, such as fitness tracking and rehabilitation. Previous methods have relied on the estimation of red-green-and-blue (RGB) frames and body pose landmarks to identify the number of action repetitions, but these methods suffer from a number of issues, including the inability to stably handle changes in camera viewpoints, over-counting, under-counting, difficulty in distinguishing between sub-actions, inaccuracy in recognizing salient poses, etc. In this paper, based on the work done by [1], we integrate joint angles with body pose landmarks to address these challenges and achieve better results than the state-of-the-art RepCount methods, with a Mean Absolute Error (MAE) of 0.211 and an Off-By-One (OBO) counting accuracy of 0.599 on the RepCount data set [2]. Comprehensive experimental results demonstrate the effectiveness and robustness of our method.