What is it about?
Owing to the fragile to external environment, current human activity recognition is still challenging in the area of computer vision. In particular, the recognition of subtle actions is a difficult task to distinguish from each other. In this paper, we proposed the methods for human action recognition based on improved sparse Gaussian Process Latent Variable Model (GPLVM) and Hidden Conditional Random Field (HCRF).
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Why is it important?
We first fused the skeletal information and the motion characteristics of human body to extract human action features from a set of action sequences. And then, we proposed an improved sparse GPLVM algorithm for feature dimensionality reduction to satisfy the visualization and computation complexity for human actions recognition. Through sparse approximation with information vector machine and the dynamic process of human actions, the improved sparse GPLVM allows the action features to be represented by the low dimensional trajectory in manifold space. Furthermore, HCRF was applied to recognize the characteristics of human actions derived from manifold spaces and finally to classify human actions.
Perspectives
The proposed methods are tested and evaluated on public action database. The experimental results show that our methods can achieve better performance of feature dimensionality reduction and visualization and obtain the average recognition accuracy of 93.68%.
Prof. Linqin CAI
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This page is a summary of: Human Action Recognition Using Improved Sparse Gaussian Process Latent Variable Model and Hidden Conditional Random Filed, IEEE Access, January 2018, Institute of Electrical & Electronics Engineers (IEEE),
DOI: 10.1109/access.2018.2822713.
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