Multiple Feature Fusion in Particle Filter Framework for Visual Tracking

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Author(s): Singaravelan Shanmugasundaram (PSR Engineering College, India), V. Selvakumar (PSR Engineering College, India), S. Balaganesh (PSR Engineering College, India), P. Gopalsamy (PSR Engineering College, India)and R. Arun (PSR Engineering College, India)
Copyright: 2024
Pages: 21
Source title: Improving Security, Privacy, and Connectivity Among Telemedicine Platforms
Source Author(s)/Editor(s): Nuno Geada (ISCTE, University Institute of Lisbon, Portugal)
DOI: 10.4018/979-8-3693-2141-6.ch012

Keywords: Knowledge Management and Library Science / Medical Information Science Reference / Medical Technologies / Telemedicine

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Abstract

Vision-based human activity recognition in smart homes has become a significant issue in terms of developing the next generation technologies Recently, deep learning models that aim to automatic extraction of low-level to high-level features of input data instead of using complicated conventional feature extraction methods have achieved significant improvements in the classification of a large amount of data especially vision-based datasets. Therefore, in this study, in order to recognize human action of a smart home video dataset. Convolutional neural networks (CNNs) architecture as a deep learning model has been proposed, and an architecture of CNNs has been proposed. Moreover, instead of using commonplace CNNs, a special CNN architecture to recognize human activity has been designed. Additionally, the performance of the proposed method has been compared with the other previous used methods on the same dataset.

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Multiple Feature Fusion in Particle Filter Framework for Visual Tracking

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