A Machine Learning Approach to Interpolating Indoors Trajectories
In this article we propose a machine learning-based modeling to solve network overload problems caused by continuous monitoring of the trajectories of multiple tracked devices indoors. The proposed modeling was evaluated with hundreds of object coordinate locations tracked in three synthetic environments and one real environment. We show that it is possible to solve the problem of network overload increasing latency in sending data and predicting as server-side trajectories with ensemble models, such as the Random Forest, and using Artificial Neural Networks. We also show that it is possible to predict at least fifteen intermediate coordinates of the paths of the tracked objects with R2 greater than 0.95.
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