Advancements in Repetitive Action Counting: Joint-Based PoseRAC Model With Improved Performance

TL;DR

This paper introduces the joint-based PoseRAC model that combines joint angles with body pose landmarks to improve repetitive action counting accuracy and robustness across different viewpoints.

Contribution

The novel integration of joint angles with pose landmarks enhances counting accuracy and addresses challenges like viewpoint changes and sub-action distinction.

Findings

Achieves MAE of 0.211 on RepCount dataset

Off-By-One accuracy of 0.599 surpassing previous methods

Demonstrates robustness across various camera viewpoints

Abstract

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.