Neuro-fuzzy in six DOF tele-robotic control

Po-Ngaen, Watcharin; Choomuang, Rerngwut; Bhuripanyo, Janjai

doi:10.1109/icma.2008.4798888

Cited by 2 publications

(1 citation statement)

References 6 publications

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“…In recent times, these neuro-fuzzy inference systems have been employed for solving problems in various domains such as load forecasting, 19 system modeling, 20,21 industrial control, 22,23 etc. A few works can also be found wherein neuro-fuzzy systems are employed for human activity prediction.…”

Section: Introductionmentioning

confidence: 99%

Human Action Recognition Using Meta-Cognitive Neuro-Fuzzy Inference System

Subramanian

Sundaram

2012

Int. J. Neur. Syst.

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We 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.

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Section: Introductionmentioning

confidence: 99%

Human Action Recognition Using Meta-Cognitive Neuro-Fuzzy Inference System

Subramanian

Sundaram

2012

Int. J. Neur. Syst.

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Human action recognition using Meta-Cognitive Neuro-Fuzzy Inference System

Subramanian

Sundaram

2012

The 2012 International Joint Conference on Neural Networks (IJCNN)

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In this paper, we propose a Meta-Cognitive NeuroFuzzy Inference System (McFIS) for accurate detection of human actions from video sequences. In this paper, we employ optical flow based features as they can represent information from local pixel level to global object level between two consecutive image planes. The functional relationship between these optical flow based features and action classes is approximated using McFIS classifier. The sequential learning algorithm is developed based on the principles of self-regulation observed in human metacognition. 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 sample. The sequential learning algorithm of McFIS is controlled and monitored by the meta-cognitive components which uses class-specific and knowledge based criteria along with self-regulatory thresholds to decide on one of the following strategies: a) sample deletion b) sample learning and c) 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 support vector machine 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.

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Neuro-fuzzy in six DOF tele-robotic control

Cited by 2 publications

References 6 publications

Human Action Recognition Using Meta-Cognitive Neuro-Fuzzy Inference System

Human Action Recognition Using Meta-Cognitive Neuro-Fuzzy Inference System

Human action recognition using Meta-Cognitive Neuro-Fuzzy Inference System

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