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FindingHuMo: Real-Time Tracking of Motion Trajectories from Anonymous Binary Sensing in Smart Environments
| Citations: | 4 - 1 self |
BibTeX
@MISC{De_findinghumo:real-time,
author = {Debraj De and Wen-zhan Song and Mingsen Xu and Cheng-liang Wang and Diane Cook and Xiaoming Huo},
title = {FindingHuMo: Real-Time Tracking of Motion Trajectories from Anonymous Binary Sensing in Smart Environments},
year = {}
}
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Abstract
Abstract—In this paper we have proposed and designed Find-ingHuMo (Finding Human Motion), a real-time user tracking system for Smart Environments. FindingHuMo can perform device-free tracking of multiple (unknown and variable number of) users in the Hallway Environments, just from non-invasive and anonymous (not user specific) binary motion sensor data stream. The significance of our designed system are as follows: (a) fast tracking of individual targets from binary motion datastream from a static wireless sensor network in the infrastructure. This needs to resolve unreliable node sequences, system noise and path ambiguity; (b) Scaling for multi-user tracking where user motion trajectories may crossover with each other in all possible ways. This needs to resolve path ambiguity to isolate overlapping trajectories; FindingHumo applies the following techniques on the collected motion datastream: (i) a proposed motion data driven adaptive order Hidden Markov Model with Viterbi decoding (called Adaptive-HMM), and then (ii) an innovative path disambiguation algorithm (called CPDA). Using this methodology the system accurately detects and isolates motion trajectories of individual users. The system performance is illustrated with results from real-time system deployment experience in a Smart Environment.
Keyphrases
smart environment motion trajectory real-time tracking anonymous binary sensing path ambiguity real-time system deployment experience innovative path disambiguation algorithm system noise multi-user tracking variable number binary motion datastream overlapping trajectory adaptive order hidden markov model binary motion sensor data stream individual target real-time user system performance static wireless sensor network fast tracking user motion trajectory motion datastream individual user motion data possible way hallway environment device-free tracking unreliable node sequence finding human motion
