Fast Human Activity Recognition Based on a Massively Parallel Implementation of Random Forest

摘要

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.