Abstract
Occlusion or camera setting produces a high degree of ambiguity when estimating human body motion from monocular video sequences. Good human motion models are an important means of addressing this problem. In this work, we propose a hierarchical motion model and a motion estimation for it to estimate human motion without camera calibration and with free camera operation. The model is able to generate particles in multi-spaces and thus is able to estimate both camera view and human motion at one time. We showed the possibility of achieving 3D motion estimation for simple movements such as ”walking” without camera calibration and with dynamic camera operation.
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Ayumi, M., Xiaojun, W., Harumi, K., Akira, K. (2013). 3D Motion Estimation of Human Body from Video with Dynamic Camera Work. In: Schwenker, F., Scherer, S., Morency, LP. (eds) Multimodal Pattern Recognition of Social Signals in Human-Computer-Interaction. MPRSS 2012. Lecture Notes in Computer Science(), vol 7742. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37081-6_9
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DOI: https://doi.org/10.1007/978-3-642-37081-6_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-37080-9
Online ISBN: 978-3-642-37081-6
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