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


Autoria(s): Subramanian, Kartick; Radhakrishnan, Venkatesh Babu; Sundaram, Suresh
Data(s)

2014

Resumo

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.

Formato

application/pdf

Identificador

http://eprints.iisc.ernet.in/51976/1/IX_IEEE_ISSNIP_2014.pdf

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.

Publicador

IEEE

Relação

http://dx.doi.org/10.1109/ISSNIP.2014.6827689

http://eprints.iisc.ernet.in/51976/

Palavras-Chave #Supercomputer Education & Research Centre
Tipo

Conference Proceedings

NonPeerReviewed