Computer Vision-based Human Activity Recognition for Elderly Home Care
L. Aneesh Euprazia1, K.K. Thyagharajan2

1L. Aneesh Euprazia, Full Time Research Scholar, RMD Engineering College, (Tamil Nadu), India.

2K.K. Thyagharajan, Dean Academic, RMD Engineering College, (Tamil Nadu), India.

Manuscript received on 25 November 2019 | Revised Manuscript received on 06 December 2019 | Manuscript Published on 14 December 2019 | PP: 299-303 | Volume-9 Issue-1S November 2019 | Retrieval Number: A10611191S19/2019©BEIESP | DOI: 10.35940/ijitee.A1061.1191S19

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Abstract: Video-based monitoring of elderly people at home receives more attention in recent days. In this paper, we propose a novel approach to develop smart monitoring system for elderly people using computer vision techniques. Gaussian Mixture Model (GMM) based algorithm is used for background and foreground separation inorder to track the activities of human object. The minimum bounding box of the human object is traced and features like major axis length, minor axis length and orientation angle are extracted. The proposed approach is evaluated on the video sequences of fall dataset.

Keywords: Aging Population, Bounding Box, Gaussian Mixture Model, Smart Monitoring.
Scope of the Article: Computer Vision