dc.contributor.authorSubramanian, K.
dc.contributor.authorSuresh, Sundaram
dc.identifier.citationSubramanian, K., & Suresh, S. (2012). Human action recognition using meta-cognitive neuro-fuzzy inference system. International journal of neural systems, 22(06).en_US
dc.description.abstractWe propose a sequential Meta-Cognitive learning algorithm for Neuro-Fuzzy Inference System (McFIS) to efficiently recognize human actions from video sequence. Optical flow information between two consecutive image planes can represent actions hierarchically from local pixel level to global object level, and hence are used to describe the human action in McFIS classifier. McFIS classifier and its sequential learning algorithm is developed based on the principles of self-regulation observed in human meta-cognition. McFIS decides on what-to-learn, when-to-learn and how-to-learn based on the knowledge stored in the classifier and the information contained in the new training samples. The sequential learning algorithm of McFIS is controlled and monitored by the meta-cognitive components which uses class-specific, knowledge based criteria along with self-regulatory thresholds to decide on one of the following strategies: (i) Sample deletion (ii) Sample learning and (iii) Sample reserve. Performance of proposed McFIS based human action recognition system is evaluated using benchmark Weizmann and KTH video sequences. The simulation results are compared with well known SVM classifier and also with state-of-the-art action recognition results reported in the literature. The results clearly indicates McFIS action recognition system achieves better performances with minimal computational effort.en_US
dc.relation.ispartofseriesInternational journal of neural systemsen_US
dc.rights© 2012 World Scientific Publishing Company.en_US
dc.subjectDRNTU::Engineering::Computer science and engineering
dc.titleHuman action recognition using meta-cognitive neuro-fuzzy inference systemen_US
dc.typeJournal Article
dc.contributor.schoolSchool of Computer Engineeringen_US

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