Vision-based Human Action Classification Using Adaptive Boosting Algorithm
KAUST DepartmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
KAUST Grant NumberOSR2015-CRG4-2582
Permanent link to this recordhttp://hdl.handle.net/10754/627907
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AbstractPrecise recognition of human action is a key enabler for the development of many applications including autonomous robots for medical diagnosis and surveillance of elderly people in home environment. This paper addresses the human action recognition based on variation in body shape. Specifically, we divide the human body into five partitions that correspond to five partial occupancy areas. For each frame, we calculated area ratios and used them as input data for recognition stage. Here, we consider six classes of activities namely: walking, standing, bending, lying, squatting, and sitting. In this paper, we proposed an efficient human action recognition scheme, which takes advantages of superior discrimination capacity of AdaBoost algorithm. We validated the effectiveness of this approach by using experimental data from two publicly available databases fall detection databases from the University of Rzeszow’s and the Universidad de Málaga fall detection datasets. We provided comparisons of the proposed approach with state-of-the-art classifiers based on the neural network, K-nearest neighbor, support vector machine and naïve Bayes and showed that we achieve better results in discriminating human gestures.
CitationZerrouki N, Harrou F, Sun Y, Houacine A (2018) Vision-based Human Action Classification Using Adaptive Boosting Algorithm. IEEE Sensors Journal: 1–1. Available: http://dx.doi.org/10.1109/JSEN.2018.2830743.
SponsorsThis publication is based upon work supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No: OSR2015-CRG4-2582. We are grateful to the two referees, the Associate Editor, and the Editor-in-Chief for their comments.
JournalIEEE Sensors Journal