ePrints@IIScePrints@IISc Home | About | Browse | Latest Additions | Advanced Search | Contact | Help

Action Recognition from Motion Capture Data using Meta-cognitive RBF Network Classifier

Vantigodi, Suraj and Radhakrishnan, Venkatesh Babu (2014) Action Recognition from Motion Capture Data using Meta-cognitive RBF Network Classifier. In: 9th IEEE International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP), APR 21-24, 2014, Singapore, SINGAPORE.

[img] PDF
IEEE_ISSNIP_2014.pdf - Published Version
Restricted to Registered users only

Download (458kB) | Request a copy
Official URL: http://dx.doi.org/10.1109/ISSNIP.2014.6827664

Abstract

Action recognition plays an important role in various applications, including smart homes and personal assistive robotics. In this paper, we propose an algorithm for recognizing human actions using motion capture action data. Motion capture data provides accurate three dimensional positions of joints which constitute the human skeleton. We model the movement of the skeletal joints temporally in order to classify the action. The skeleton in each frame of an action sequence is represented as a 129 dimensional vector, of which each component is a 31) angle made by each joint with a fixed point on the skeleton. Finally, the video is represented as a histogram over a codebook obtained from all action sequences. Along with this, the temporal variance of the skeletal joints is used as additional feature. The actions are classified using Meta-Cognitive Radial Basis Function Network (McRBFN) and its Projection Based Learning (PBL) algorithm. We achieve over 97% recognition accuracy on the widely used Berkeley Multimodal Human Action Database (MHAD).

Item Type: Conference Proceedings
Additional Information: Copy right for this article belongs to the IEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA
Keywords: Human action recognition; motion capture; Meta-Cognitive Radial Basis Function Network; Projection Based Learning
Department/Centre: Division of Interdisciplinary Research > Supercomputer Education & Research Centre
Depositing User: Id for Latest eprints
Date Deposited: 31 Jul 2015 14:19
Last Modified: 31 Jul 2015 14:19
URI: http://eprints.iisc.ac.in/id/eprint/51978

Actions (login required)

View Item View Item