Human action recognition employing 2DPCA and VQ in the spatio-temporal domain

Naiel M.A.

Abdelwahab M.M.

Mikhael W.B.

In this paper a novel algorithm for human action recognition is presented. This approach is based on Two-Dimensional Principal Component Analysis (2DPCA) and Vector Quantization (VQ) in the spatial-temporal domain. This method reduces computational complexity by a factor of 98, while maintaining the storage requirement and the recognition accuracy, compared with some of the most recent approaches in the field. Experimental results applied on the Weizmann dataset confirm the excellent properties of the proposed algorithm. © 2010 IEEE.

Human action recognition employing 2DPCA and VQ in the spatio-temporal

Human action recognition employing 2DPCA and VQ in the spatio-temporal domain

A Deep Learning-Based Benchmarking Framework for Lane Segmentation in the Complex and Dynamic Road Scenes

Multi projection fusion for real-time semantic segmentation of 3D LiDAR point clouds

An Analytical Computational Algorithm for Solving a System of Multipantograph DDEs Using Laplace Variational Iteration Algorithm

Multimodal Video Sentiment Analysis Using Deep Learning Approaches, a Survey

Human action recognition employing 2DPCA and VQ in the spatio-temporal

Human action recognition employing 2DPCA and VQ in the spatio-temporal domain

Related Publications

A Deep Learning-Based Benchmarking Framework for Lane Segmentation in the Complex and Dynamic Road Scenes

Multi projection fusion for real-time semantic segmentation of 3D LiDAR point clouds

An Analytical Computational Algorithm for Solving a System of Multipantograph DDEs Using Laplace Variational Iteration Algorithm

Multimodal Video Sentiment Analysis Using Deep Learning Approaches, a Survey