Trajectory inference using a motion sensing network
D Cox, D Fairall, N MacMillan… - … on Computer and …, 2014 - ieeexplore.ieee.org
D Cox, D Fairall, N MacMillan, D Marinakis, D Meger, S Pourtavakoli, K Weston
2014 Canadian Conference on Computer and Robot Vision, 2014•ieeexplore.ieee.orgThis 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 …
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 …
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.
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