An Optical How Feature and McFIS Based Approach for 3-Dimensional Human Action Recognition

Subramanian, Kartick and Radhakrishnan, Venkatesh Babu and Sundaram, Suresh (2014) An Optical How Feature and McFIS Based Approach for 3-Dimensional Human Action Recognition. In: 9th IEEE International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP), APR 21-24, 2014.

PDF IX_IEEE_ISSNIP_2014.pdf - Published Version Restricted to Registered users only Download (197kB) | Request a copy

Abstract

We propose to develop a 3-D optical flow features based human action recognition system. Optical flow based features are employed here since they can capture the apparent movement in object, by design. Moreover, they can represent information hierarchically from local pixel level to global object level. In this work, 3-D optical flow based features a re extracted by combining the 2-1) optical flow based features with the depth flow features obtained from depth camera. In order to develop an action recognition system, we employ a Meta-Cognitive Neuro-Fuzzy Inference System (McFIS). The m of McFIS is to find the decision boundary separating different classes based on their respective optical flow based features. McFIS consists of a neuro-fuzzy inference system (cognitive component) and a self-regulatory learning mechanism (meta-cognitive component). During the supervised learning, self-regulatory learning mechanism monitors the knowledge of the current sample with respect to the existing knowledge in the network and controls the learning by deciding on sample deletion, sample learning or sample reserve strategies. The performance of the proposed action recognition system was evaluated on a proprietary data set consisting of eight subjects. The performance evaluation with standard support vector machine classifier and extreme learning machine indicates improved performance of McFIS is recognizing actions based of 3-D optical flow based features.

Item Type:	Conference Proceedings
Publisher:	IEEE
Additional Information:	Copy right for this article belongs to the IEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA
Keywords:	3-D action recognition; meta-cognition; neural fuzzy system; classification; video analytics
Department/Centre:	Division of Interdisciplinary Sciences > Supercomputer Education & Research Centre
Date Deposited:	31 Jul 2015 14:15
Last Modified:	31 Jul 2015 14:15
URI:	http://eprints.iisc.ac.in/id/eprint/51976

Actions (login required)

View Item