Spatio-Temporal Constrained Human Trajectory Generation from the PIR Motion Detector Sensor Network Data: A Geometric Algebra Approach

Passive infrared (PIR) motion detectors, which can support long-term continuous observation, are widely used for human motion analysis. Extracting all possible trajectories from the PIR sensor networks is important. Because the PIR sensor does not log location and individual information, none of the...

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Main Authors: Zhaoyuan Yu, Linwang Yuan, Wen Luo, Linyao Feng, Guonian Lv
Format: Article
Language:English
Published: MDPI AG 2015-12-01
Series:Sensors
Subjects:
Online Access:http://www.mdpi.com/1424-8220/16/1/43
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spelling doaj-36c9a1ea207549edb46124bc637de96f2020-11-25T01:13:32ZengMDPI AGSensors1424-82202015-12-011614310.3390/s16010043s16010043Spatio-Temporal Constrained Human Trajectory Generation from the PIR Motion Detector Sensor Network Data: A Geometric Algebra ApproachZhaoyuan Yu0Linwang Yuan1Wen Luo2Linyao Feng3Guonian Lv4Key Laboratory of VGE (Ministry of Education), Nanjing Normal University, No.1 Wenyuan Road, Nanjing 210023, ChinaKey Laboratory of VGE (Ministry of Education), Nanjing Normal University, No.1 Wenyuan Road, Nanjing 210023, ChinaKey Laboratory of VGE (Ministry of Education), Nanjing Normal University, No.1 Wenyuan Road, Nanjing 210023, ChinaKey Laboratory of VGE (Ministry of Education), Nanjing Normal University, No.1 Wenyuan Road, Nanjing 210023, ChinaKey Laboratory of VGE (Ministry of Education), Nanjing Normal University, No.1 Wenyuan Road, Nanjing 210023, ChinaPassive infrared (PIR) motion detectors, which can support long-term continuous observation, are widely used for human motion analysis. Extracting all possible trajectories from the PIR sensor networks is important. Because the PIR sensor does not log location and individual information, none of the existing methods can generate all possible human motion trajectories that satisfy various spatio-temporal constraints from the sensor activation log data. In this paper, a geometric algebra (GA)-based approach is developed to generate all possible human trajectories from the PIR sensor network data. Firstly, the representation of the geographical network, sensor activation response sequences and the human motion are represented as algebraic elements using GA. The human motion status of each sensor activation are labeled using the GA-based trajectory tracking. Then, a matrix multiplication approach is developed to dynamically generate the human trajectories according to the sensor activation log and the spatio-temporal constraints. The method is tested with the MERL motion database. Experiments show that our method can flexibly extract the major statistical pattern of the human motion. Compared with direct statistical analysis and tracklet graph method, our method can effectively extract all possible trajectories of the human motion, which makes it more accurate. Our method is also likely to provides a new way to filter other passive sensor log data in sensor networks.http://www.mdpi.com/1424-8220/16/1/43sensor networkstrajectory recoveringgeometric algebraspatio-temporal constraintstrajectory filteringMERL motion sensor
collection DOAJ
language English
format Article
sources DOAJ
author Zhaoyuan Yu
Linwang Yuan
Wen Luo
Linyao Feng
Guonian Lv
spellingShingle Zhaoyuan Yu
Linwang Yuan
Wen Luo
Linyao Feng
Guonian Lv
Spatio-Temporal Constrained Human Trajectory Generation from the PIR Motion Detector Sensor Network Data: A Geometric Algebra Approach
Sensors
sensor networks
trajectory recovering
geometric algebra
spatio-temporal constraints
trajectory filtering
MERL motion sensor
author_facet Zhaoyuan Yu
Linwang Yuan
Wen Luo
Linyao Feng
Guonian Lv
author_sort Zhaoyuan Yu
title Spatio-Temporal Constrained Human Trajectory Generation from the PIR Motion Detector Sensor Network Data: A Geometric Algebra Approach
title_short Spatio-Temporal Constrained Human Trajectory Generation from the PIR Motion Detector Sensor Network Data: A Geometric Algebra Approach
title_full Spatio-Temporal Constrained Human Trajectory Generation from the PIR Motion Detector Sensor Network Data: A Geometric Algebra Approach
title_fullStr Spatio-Temporal Constrained Human Trajectory Generation from the PIR Motion Detector Sensor Network Data: A Geometric Algebra Approach
title_full_unstemmed Spatio-Temporal Constrained Human Trajectory Generation from the PIR Motion Detector Sensor Network Data: A Geometric Algebra Approach
title_sort spatio-temporal constrained human trajectory generation from the pir motion detector sensor network data: a geometric algebra approach
publisher MDPI AG
series Sensors
issn 1424-8220
publishDate 2015-12-01
description Passive infrared (PIR) motion detectors, which can support long-term continuous observation, are widely used for human motion analysis. Extracting all possible trajectories from the PIR sensor networks is important. Because the PIR sensor does not log location and individual information, none of the existing methods can generate all possible human motion trajectories that satisfy various spatio-temporal constraints from the sensor activation log data. In this paper, a geometric algebra (GA)-based approach is developed to generate all possible human trajectories from the PIR sensor network data. Firstly, the representation of the geographical network, sensor activation response sequences and the human motion are represented as algebraic elements using GA. The human motion status of each sensor activation are labeled using the GA-based trajectory tracking. Then, a matrix multiplication approach is developed to dynamically generate the human trajectories according to the sensor activation log and the spatio-temporal constraints. The method is tested with the MERL motion database. Experiments show that our method can flexibly extract the major statistical pattern of the human motion. Compared with direct statistical analysis and tracklet graph method, our method can effectively extract all possible trajectories of the human motion, which makes it more accurate. Our method is also likely to provides a new way to filter other passive sensor log data in sensor networks.
topic sensor networks
trajectory recovering
geometric algebra
spatio-temporal constraints
trajectory filtering
MERL motion sensor
url http://www.mdpi.com/1424-8220/16/1/43
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