Abstract
We proposed a method for Human action Recognition. It is based on the construction of a set of templates for each activity. Each template is constructed based on the Accumulated Motion Image of the Video. Each template contains where motion has occurred in the video. FFT Transform is applied to each template. A 3D Spatiotemporal Volume is generated for each class. A Single action Maximum average Correlation height Filter is generated for each class. The filter is applied to the test video and using the threshold the actions are classified. The experiments are conducted on Weizmann dataset.
Highlights
Human motion analysis and recognition have become a highly active area in computer vision, due to the presence of Surveillance cameras and personal video devices
Human action recognition is the process of identifying human actions that occur in the video sequences
The MACH filter is created in the frequency
Summary
Human motion analysis and recognition have become a highly active area in computer vision, due to the presence of Surveillance cameras and personal video devices. The application domains are in Surveillance footage, Userinterfaces, Robotics, Automatic video organization, patient monitoring systems, athletic performance analysis etc. Aggarwal and Ryoo (2011) provided an overview of the current approaches to Human activity Recognition. They have explored the various methodologies that is used in human action recognition. Shrivastava and Singh (2012) analysed the performance of three methods of human action recognition. The objective of this research work is to propose a Human Action Recognition method Using Temporal Partitioning of activities and Maximum average Correlation Height Filter for Weizmann dataset
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