Three-dimensional human motion key frame extracting method based on fuse of improved LLE and PCA

Abstract

The invention discloses a motion key frame extracting method of fusing the improved locally linear embedding (LLE for short) and principal component analysis (PCA for short). The extracting method comprises the steps as follows: 1, using the improved LLE for pre-processing the original motion capturing data and reducing dimension to obtain the data with fixed dimension; 2, using PCA method for processing the data with reduced dimension to extract the one-dimensional main component, using the flat filtering method for removing the noise to obtain the one-dimensional characteristic curve; 3, extracting the extreme point of the characteristic curve to obtain the initial key frame;, 4, inserting the corresponding frame number between the initial key frames according to the characteristic curve amplitude difference and set threshold value, merging the overstocked key frame to obtain the final key frame set.

Description

technical field [0001] The invention relates to key frame extraction related to human body motion, in particular to a 3D human body motion key frame extraction method based on fusion improved LLE and PCA. Background technique [0002] The emergence of key frame extraction technology makes the size of the motion capture database smaller. Key frame extraction is to select the most important and critical frame in a motion sequence as a key frame, representing the entire motion sequence. Good visual generalization, and at the same time, motion reconstruction can be performed to restore the original motion and maintain a low error rate. [0003] At present, the sampling methods are mainly divided into two categories: equal interval sampling and adaptive sampling. The problem of oversampling and undersampling may occur in equal interval sampling. Adaptive sampling can sample less in places with small changes and more samples in places with large changes, so it can solve the short...

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Problems solved by technology

For the problems of the classic LLE algorithm, such as "LLE" published by Jiangnan University in China in 2011 1 Fan Local Linear Embedding", "LLE Improved Algorithm Based on Sparse Constraints" published by Ningbo University in 2013, and "Robust locally linear embedding using penalty functions" published by the University of Waterloo abroad in 2011 have all improved the LLE algorithm to a certain extent. Robustness, but it cannot obtain stable results and does not reduce the selection of the number of neighbor points

Another example is "Local Linear Embedding of Neighborhood Parameter Dynamic Change" published by South China University of Technology in 2008. Although the selection of the number of neighbor points is well controlled, the results often have a certain degree of randomness.

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