Rosdiyana, Samad and Law, Wen Yan and Mahfuzah, Mustafa and Nor Rul Hasma, Abdullah and Pebrianti, Dwi (2018) Multiple human body postures detection using kinect. Indonesian Journal of Electrical Engineering and Computer Science, 10 (2). pp. 528-536. ISSN 2502-4752. (Published)
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Abstract
This paper presents a method to detect multiple human body postures using Kinect sensor. In this study, a combination of shape features and body joint points are used as input features. The Kinect sensor which used infrared camera to produce a depth image is suitable to be used in an environment that has varying lighting conditions. The method for human detection is done by processing the depth image and joint data (skeleton) which able to overcome several problems such as cluttered background, various articulated poses, and change in color and illumination. Then, the body joint coordinates found on the object are used to calculate the body proportion ratio. In the experiment, the average body proportions from three body parts are obtained to verify the suitableness of golden ratio usage in this work. Finally, the measured body proportion is compared with Golden Ratio to determine whether the found object is a real human body or not. This method is tested for various scenarios, where true positive human detection is high for various postures. This method able to detect a human body in low lighting and dark room. The average body proportions obtained from the experiment show that the value is close to the golden ratio value.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | Human detection; Kinect sensor; Golden ratio; Skeleton detection; Multiple postures |
| Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Faculty/Division: | Faculty of Electrical & Electronic Engineering |
| Depositing User: | Mrs. Neng Sury Sulaiman |
| Date Deposited: | 15 Aug 2018 07:29 |
| Last Modified: | 15 Aug 2018 07:29 |
| URI: | http://umpir.ump.edu.my/id/eprint/20909 |
| Download Statistic: | View Download Statistics |
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