 Intelligent wristband human abnormal behaviour recognition method based on machine visionChun-Ling Liu and Chun-Bao Huo International Journal of Product Development, 2022, vol. 26, issue 1/2/3/4, 254-267 Abstract: In order to overcome the problems of low recognition accuracy and high-recall rate of traditional methods, an intelligent wristband human abnormal behaviour recognition method based on machine vision is proposed. Firstly, the human behaviour video in the smart wristband is obtained through intensive sampling, and the image feature descriptor is obtained according to regularisation processing; the human abnormal behaviour features are extracted by DT algorithm; secondly, softmax classifier is introduced to classify the extracted human abnormal line features of intelligent wristband; the dense trajectory algorithm is used to extract the abnormal behaviour characteristics of intelligent wristband human body, and the machine vision is introduced to recognise the abnormal behaviour of intelligent wristband human body. The experimental results show that the recognition accuracy of this method is as high as 98.2%, and the recognition recall rate is only 4%, which shows that this method can effectively improve the recognition effect. Keywords: smartband; machine vision; softmax classifier; abnormal behaviour; weighted processing method. (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:ids:ijpdev:v:26:y:2022:i:1/2/3/4:p:254-267 Access Statistics for this article More articles in International Journal of Product Development from Inderscience Enterprises Ltd |
Is your work missing from RePEc? Here is how to contribute.
Questions or problems? Check the EconPapers FAQ or send mail to .
EconPapers is hosted by the
School of Business at Örebro University.