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Open AccessArticle

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

by 1,2,3, 1,2,3, 1,2,*, 1 and 1,2,3
1
Key Laboratory of VGE (Ministry of Education), Nanjing Normal University, No.1 Wenyuan Road, Nanjing 210023, China
2
State Key Laboratory Cultivation Base of Geographical Environment Evolution (Jiangsu Province), No.1 Wenyuan Road, Nanjing 210023, China
3
Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, No.1 Wenyuan Road, Nanjing 210023, China
*
Author to whom correspondence should be addressed.
Academic Editor: Leonhard M. Reindl
Sensors 2016, 16(1), 43; https://doi.org/10.3390/s16010043
Received: 30 October 2015 / Revised: 7 December 2015 / Accepted: 25 December 2015 / Published: 30 December 2015
(This article belongs to the Section Sensor Networks)
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. View Full-Text
Keywords: sensor networks; trajectory recovering; geometric algebra; spatio-temporal constraints; trajectory filtering; MERL motion sensor sensor networks; trajectory recovering; geometric algebra; spatio-temporal constraints; trajectory filtering; MERL motion sensor
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MDPI and ACS Style

Yu, Z.; Yuan, L.; Luo, W.; Feng, L.; Lv, G. Spatio-Temporal Constrained Human Trajectory Generation from the PIR Motion Detector Sensor Network Data: A Geometric Algebra Approach. Sensors 2016, 16, 43. https://doi.org/10.3390/s16010043

AMA Style

Yu Z, Yuan L, Luo W, Feng L, Lv G. Spatio-Temporal Constrained Human Trajectory Generation from the PIR Motion Detector Sensor Network Data: A Geometric Algebra Approach. Sensors. 2016; 16(1):43. https://doi.org/10.3390/s16010043

Chicago/Turabian Style

Yu, Zhaoyuan; Yuan, Linwang; Luo, Wen; Feng, Linyao; Lv, Guonian. 2016. "Spatio-Temporal Constrained Human Trajectory Generation from the PIR Motion Detector Sensor Network Data: A Geometric Algebra Approach" Sensors 16, no. 1: 43. https://doi.org/10.3390/s16010043

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