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...
| Main Authors: | , , , , |
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| Format: | Article |
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MDPI AG
2015-12-01
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| Series: | Sensors |
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| Online Access: | http://www.mdpi.com/1424-8220/16/1/43 |
| _version_ | 1798025890510143488 |
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| author | Zhaoyuan Yu Linwang Yuan Wen Luo Linyao Feng Guonian Lv |
| author_facet | Zhaoyuan Yu Linwang Yuan Wen Luo Linyao Feng Guonian Lv |
| author_sort | Zhaoyuan Yu |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-04-11T18:26:13Z |
| format | Article |
| id | doaj.art-36c9a1ea207549edb46124bc637de96f |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T18:26:13Z |
| publishDate | 2015-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-36c9a1ea207549edb46124bc637de96f2022-12-22T04:09:36ZengMDPI 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 |
| 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 |
| title | 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_short | 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 |
| 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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