Real-time upper-body human pose estimation from depth data using Kalman filter for simulator

D. Lee; S. Chi; C. Park; H. Yoon; J. Kim; C. H. Park

doi:10.1117/12.2064621

22 August 2014 Real-time upper-body human pose estimation from depth data using Kalman filter for simulator

D. Lee, S. Chi, C. Park, H. Yoon, J. Kim, C. H. Park

Proceedings Volume 9286, Second International Conference on Applications of Optics and Photonics; 928625 (2014) https://doi.org/10.1117/12.2064621
Event: Second International Conference on Applications of Optics and Photonics, 2014, Aveiro, Portugal

Abstract

Recently, many studies show that an indoor horse riding exercise has a positive effect on promoting health and diet. However, if a rider has an incorrect posture, it will be the cause of back pain. In spite of this problem, there is only few research on analyzing rider’s posture. Therefore, the purpose of this study is to estimate a rider pose from a depth image using the Asus’s Xtion sensor in real time. In the experiments, we show the performance of our pose estimation algorithm in order to comparing the results between our joint estimation algorithm and ground truth data.

Citation Download Citation

D. Lee, S. Chi, C. Park, H. Yoon, J. Kim, and C. H. Park "Real-time upper-body human pose estimation from depth data using Kalman filter for simulator", Proc. SPIE 9286, Second International Conference on Applications of Optics and Photonics, 928625 (22 August 2014); https://doi.org/10.1117/12.2064621

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