Please use this identifier to cite or link to this item: https://ir.swu.ac.th/jspui/handle/123456789/12491
ชื่อเรื่อง: A Robus Method for Wheelchair Detection: A Combination of the Gaussian Mixture Models and Histogram of Oriented Gradients
ผู้แต่ง: Hirunwattanakun S.
Chianrabutra C.
Charoenpong T.
Chanwimalueng T.
Keywords: Artificial intelligence
Graphic methods
Robotics
Support vector machines
detect object
Feature vector extraction
Gaussian mixture model (GMMs)
Histogram of oriented gradients
Histogram of oriented gradients (HOG)
Visual surveillance systems
Wheelchair detections
Wheelchair users
Wheelchairs
วันที่เผยแพร่: 2019
บทคัดย่อ: An important function for a smart health care system, aiming to maximize safety and comfort to elderly or people with dysfunctional legs, is the automatic detection of a wheelchair captured from a visual surveillance system. In this paper, we proposed a method for detecting a two-dimensional wheelchair image using a combination of the Gaussian Mixture Models (GMMs) and the Histogram of Oriented Gradients (HOG). The proposed method consists of three main steps: (i). foreground segmentation, (ii). feature vector extraction, and (iii). wheelchair detection. The GMMs technique was used to extract a moving object from a background, while the underlying feature vectors of the moving objects were obtained using the HOG method. Finally, the Support Vector Machines (SVM) was implemented to classify a wheelchair object. We implemented 1,217 images for evaluating the performance of our proposed method which results in 86.01% of the accuracy rate. The advantage of our proposed approach is that it can detect a wheelchair effectively without any knowledge or prior information of the previous frames. © 2019 IEEE.
URI: https://ir.swu.ac.th/jspui/handle/123456789/12491
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85063302833&doi=10.1109%2fICA-SYMP.2019.8646053&partnerID=40&md5=0f12ce71322f57e0216374dc03bfe33e
Appears in Collections:Scopus 1983-2021

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