Publication: A Robus Method for Wheelchair Detection: A Combination of the Gaussian Mixture Models and Histogram of Oriented Gradients
0
0
Issued Date
2019
Resource Type
File Type
application/pdf
Other identifier(s)
2-s2.0-85063302833
Rights Holder(s)
Scopus
Bibliographic Citation
2019 1st International Symposium on Instrumentation, Control, Artificial Intelligence, and Robotics, ICA-SYMP 2019. (2019), p.57-60
Suggested Citation
Hirunwattanakun S., Chianrabutra C., Charoenpong T., Chanwimalueng T. A Robus Method for Wheelchair Detection: A Combination of the Gaussian Mixture Models and Histogram of Oriented Gradients. 2019 1st International Symposium on Instrumentation, Control, Artificial Intelligence, and Robotics, ICA-SYMP 2019. (2019), p.57-60. doi:10.1109/ICA-SYMP.2019.8646053 Retrieved from: https://hdl.handle.net/20.500.14740/5459
Abstract
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.