Multi-View Feature Clustering Technique for Detection and Classification of Human Actions

Ahmed, Syed and Guptha, Nirmala and Fathima, Afifa and Ashwini, S (2021) Multi-View Feature Clustering Technique for Detection and Classification of Human Actions. In: ICASISET 2020, 16-17 May 2020, Chennai, India.

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Abstract

Recognizing the actions performed by any person is the most suc-cessful applications in pattern recognition. Detecting the action in a moving camera influences dynamic view changes, is based on spatio-temporal infor-mation at multiple temporal scales. In this paper, we are presenting a system that is dependent on actions based on multi-view information. These multi-view features are extracted from various temporal scales. The GMM and Prewitt edge filter is used for detecting background and foreground image. The Nearest Mean Classifier is used to cluster features vector’s of moving object. The experiment results demonstrated using Kth dataset producing 98% of accuracy.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: human action detection pattern recognition clustering classification
Subjects: Q Science > QA Mathematics > QA76 Computer software
Depositing User: EAI Editor III.
Date Deposited: 09 Mar 2021 09:47
Last Modified: 09 Mar 2021 09:47
URI: https://eprints.eudl.eu/id/eprint/1376

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