Paper
24 November 2014 3D Gaussian bin based sparse representation for action recognition
Min Jiang, Jun Kong, Yi-Ning Sun
Author Affiliations +
Proceedings Volume 9301, International Symposium on Optoelectronic Technology and Application 2014: Image Processing and Pattern Recognition; 930108 (2014) https://doi.org/10.1117/12.2065915
Event: International Symposium on Optoelectronic Technology and Application 2014, 2014, Beijing, China
Abstract
We propose a new representation 3DGBOJ to quickly and precisely classify human action from a series of depth maps. We use Shotton et al's method to predict the best candidate of 3D skeletal joint locations from Kinect depth map. By normalizing and retargeting the human skeleton to a common skeleton, we eliminate the noisy introduced by human agent diversity and view dependent. Some impossible motions are deleted with regard to Kinematics constraint. We design a 3D Gaussian space to map each joint to a bin based sparse feature vector. To weaken the timescale variation, which occurs during the performance with different speed and style, we remove the consecutive repeated vectors. We cluster the motion feature vectors with Affinity Propagation and treat each motion exemplar as a vocabulary in bag of feature (BOF). To better handle overlapping features and contextual dependencies, we trained them over a linear CRFs model. The experiment result shows that our representation maintains appropriate adaptability to variations of different subjects of different gender and size, and with different speed and style from different view.
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Min Jiang, Jun Kong, and Yi-Ning Sun "3D Gaussian bin based sparse representation for action recognition", Proc. SPIE 9301, International Symposium on Optoelectronic Technology and Application 2014: Image Processing and Pattern Recognition, 930108 (24 November 2014); https://doi.org/10.1117/12.2065915
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KEYWORDS
3D modeling

3D image processing

Video

Visualization

Lithium

Image segmentation

Kinematics

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