Body part extraction and pose estimation method in rowing videos

Abstract

This paper describes an image processing approach capable of estimating the pose of athletes exercising on indoor rowing machines in video sequences. The proposed algorithm finds and tracks the wrist, elbow, shoulder, ankle, knee, hip and head, and the line of the back also. Our contribution is twofold. The first contribution is a new background subtraction method, which can reliably separate the silhouette of athletes under some assumptions related to the videos. Furthermore, the paper introduces - as the second contribution - a skeleton fitting method to find the joints of the athletes based on the results of the background subtraction. This algorithm is based on anthropometric data and special movement patterns. The overall solution works on a real time setting in the test environment. Comparing the results, it is shown that our method surpasses the most accurate state-of-the-art general pose estimation solution for indoor rowing specific videos based on two commonly used metrics, as well.