Abstract
This article elaborates on the task of Human Activity Recognition being solved with the Random Forest algorithm. A performance measure is provided in terms of both recognition accuracy and computation speed. In addition, the Random Forest algorithm was implemented using CUDA, a technology providing options for massively parallel computations on low-cost hardware. The results suggest that Random Forest is a suitable and highly reliable technique for recognising human activities and that Graphics Processing Units can significantly improve the computation times of this otherwise rather time-consuming algorithm.
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Acknowledgement
This work was supported by the IT4Innovations Centre of Excellence project (CZ.1.05/1.1.00/02.0070), funded by the European Regional Development Fund and the national budget of the Czech Republic via the Research and Development for Innovations Operational Programme and by Project SP2015/105 “DPDM - Database of Performance and Dependability Models” of the Student Grand System, VŠB Technical University of Ostrava and by Project SP2015/146 “Parallel processing of Big data 2” of the Student Grand System, VŠB Technical University of Ostrava.
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Janoušek, J., Gajdoš, P., Dohnálek, P., Radecký, M. (2016). Fast Human Activity Recognition Based on a Massively Parallel Implementation of Random Forest. In: Nguyen, N.T., Trawiński, B., Fujita, H., Hong, TP. (eds) Intelligent Information and Database Systems. ACIIDS 2016. Lecture Notes in Computer Science(), vol 9622. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-49390-8_16
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