Trajectory Inference Using a Motion Sensing Network

Doug Cox, Darren Fairall, Neil MacMillan, Dimitri Marinakis, David Meger, Saamaan Pourtavakoli, Kyle Weston

Abstract: This paper addresses the problem of inferring human trajectories through an environment using low frequency, low fidelity data from a sensor network. We present a novel "recombine" proposal for Markov Chain construction and use the new proposal to devise a probabilistic trajectory inference algorithm that generates likely trajectories given raw sensor data. We also propose a novel, low-power, long range, 900 MHz IEEE 802.15.4 compliant sensor network that makes outdoors deployment viable. Finally, we present experimental results from our deployment at a retail environment.