Abstract
In this paper, we present human pose estimation algorithm that use depth information only. To estimate human poses on a low cost computing platform, we propose a human pose estimation algorithm that mixes a geodesic graph and a support vector machine (SVM). The proposed algorithm can work for any human without calibration and thus anyone can use the system immediately. The SVM-based human pose estimator uses randomly selected human features to reduce computation. The human pose estimation is evaluated through several experiments and the results showed that our approaches perform fairly well.
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Kim, H., Lee, S., Kim, Y., Lee, D., Ju, J., Myung, H. (2015). Human Pose Estimation Algorithm for Low-Cost Computing Platform Using Depth Information Only. In: Kim, JH., Yang, W., Jo, J., Sincak, P., Myung, H. (eds) Robot Intelligence Technology and Applications 3. Advances in Intelligent Systems and Computing, vol 345. Springer, Cham. https://doi.org/10.1007/978-3-319-16841-8_61
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DOI: https://doi.org/10.1007/978-3-319-16841-8_61
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-16840-1
Online ISBN: 978-3-319-16841-8
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