Multi-person motion capture method based on three-dimensional hypothesis spatial clustering

A technology of spatial clustering and motion capture, applied in the field of multi-person motion capture, can solve the problems of large differences in individual body proportions and sizes, small scale, and high three-dimensional posture state parameters and dimensions, and achieve the effect of stable and robust posture tracking.

Active Publication Date: 2019-07-16
ZHEJIANG UNIV
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Problems solved by technology

However, the 3D pose data set is difficult to obtain and the scale is small; at the same time, the state parameters of the 3D pose have a high dimensionality, and the individual body proportions and sizes vary greatly
This leads t

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  • Multi-person motion capture method based on three-dimensional hypothesis spatial clustering
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  • Multi-person motion capture method based on three-dimensional hypothesis spatial clustering

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Embodiment Construction

[0036]The present invention aims at estimating credible multi-person 3D human poses satisfying multi-view geometry constraints and bone length constraints. First of all, the present invention proposes a fully automatic multi-person human body motion capture method, which does not rely on any human body model or prior knowledge of human bone length, color, body shape, etc., does not require manual intervention, human body segmentation and other operations, and has a high degree of flexibility and practicality. Secondly, the present invention proposes a simple and efficient technique for associating two-dimensional bone key points between multiple views. Articulation point estimation is very robust. Finally, the present invention proposes a reliable multi-person pose reconstruction and tracking technology, which reconstructs the three-dimensional human poses of multiple people by comprehensively considering multi-view geometric constraints, bone length constraints and multi-vie...

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Abstract

The invention provides a multi-person motion capture method based on three-dimensional hypothesis spatial clustering. The method can be used for unmarked human motion capture. The method comprises thesteps of associating two-dimensional joint point candidate points between different views, reconstructing three-dimensional joint point candidate points, and carrying out three-dimensional attitude analysis and attitude tracking. Under the condition that a human body model is not utilized or any human body prior knowledge is assumed, stable and credible two-dimensional and global three-dimensional human body posture estimation can be carried out on a plurality of persons with different figures and unfixed number of people. The generated posture meets the multi-view geometric constraint and the human body bone length constraint, and robust and credible human body posture estimation under challenging scenes such as multi-person mutual shielding and close interaction is achieved.

Description

technical field [0001] The invention relates to a multi-person motion capture method based on three-dimensional hypothetical space clustering. Background technique [0002] According to different input data, the existing 3D human pose estimation methods can be divided into: based on monocular RGB image (sequence); based on depth image (sequence); and based on multi-view image (sequence). Estimating 3D human body pose based on monocular RGB images (sequences) is a problem with serious constraints. The observation input of the system is a complex natural image, and the state output is a high-dimensional human body pose. The process from observation input to state output is highly nonlinear. . Insufficient 3D pose training data sets, differences in the size and proportion of different human bodies, and the high dimensionality of the 3D pose space all make the reliability of 3D pose reconstruction a key problem to be solved. The 3D human pose estimation method based on depth i...

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Application Information

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IPC IPC(8): G06K9/00G06N3/04
CPCG06V40/103G06N3/045
Inventor 刘新国李妙鹏周子孟
Owner ZHEJIANG UNIV
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