Open AccessA Slow-Motion Detecting Algorithm using High Order Statistic Approach
Author(s) -
Fuqing Yuan,
Jinmei Lu
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1827/1/012152
Subject(s) - computer science , kurtosis , motion (physics) , algorithm , statistic , noise (video) , internet of things , motion detection , statistical hypothesis testing , artificial intelligence , signal (programming language) , higher order statistics , computer vision , electronics , signal processing , mathematics , statistics , engineering , embedded system , telecommunications , radar , electrical engineering , programming language , image (mathematics)
Motion detection is vital for consumer electronics and the Internet of things (IOT). For a scenario where the motion is slow and gentle, the resolution of the motion sensor is critical for the detection, while the algorithm development is another critical issue to differentiate the motion signal from noise measurement. This paper investigates the feasibility of using higher order statistics kurtosis as a motion indicator. Statistical hypothesis test has been developed to assess the motion presence. Several experiments are conducted to test the feasibility and performance of the approach. The results show the approach is feasible, but with some limitations.