Abstract
This paper presents a marker-less approach for human body pose estimation. It employs skeletons extracted from 2D binary silhouettes of videos and uses a classification method to partition the resultant skeletons into five regions namely, the spine and four limbs. The classification method also identifies the neck, the head and the shoulders. Using the center of mass principles, a model is fitted to the body parts. The spine is modeled with a 2nd order curve while each limb is modeled by two intersected lines. Finally, the model parameters represented by a reference point and two angles belonging to the lines are estimated and the pose is reconstructed. The proposed approach can estimate body poses from single images as well as multiple frames and is considerably robust to occlusions. Unlike existing methods, our approach is computationally efficient and can track human motion while correcting for pose errors using multiple frames. The proposed approach was tested on real videos from MuHAVi and MAS databases and gave promising results.
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El-Sallam, A.A., Mian, A.S. (2010). Human Body Pose Estimation from Still Images and Video Frames. In: Campilho, A., Kamel, M. (eds) Image Analysis and Recognition. ICIAR 2010. Lecture Notes in Computer Science, vol 6111. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13772-3_19
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DOI: https://doi.org/10.1007/978-3-642-13772-3_19
Publisher Name: Springer, Berlin, Heidelberg
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