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Multiple Human Tracking Using Binary Infrared Sensors.

Miyazaki T, Kasama Y - Sensors (Basel) (2015)

Bottom Line: Our algorithm can estimate multiple human movement paths without a priori knowledge of the number of humans in the room.By repeating predictions and estimations of human positions and links from the previous human positions to the estimated ones at each time period, human movement paths can be estimated.Simulation-based evaluation results show that our algorithm can dynamically trace human movement paths.

View Article: PubMed Central - PubMed

Affiliation: The University of Aizu, Aizuwakamatsu, Fukushima 965-8580, Japan. miyazaki@u-aizu.ac.jp.

ABSTRACT
To create a context-aware environment, human locations and movement paths must be considered. In this paper, we propose an algorithm that tracks human movement paths using only binary sensed data obtained by infrared (IR) sensors attached to the ceiling of a room. Our algorithm can estimate multiple human movement paths without a priori knowledge of the number of humans in the room. By repeating predictions and estimations of human positions and links from the previous human positions to the estimated ones at each time period, human movement paths can be estimated. Simulation-based evaluation results show that our algorithm can dynamically trace human movement paths.

No MeSH data available.


Example of TrackTarget().
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sensors-15-13459-f008: Example of TrackTarget().

Mentions: Figure 8 shows an example of the Path Estimation step. Here, TDL = {td1, td2} and CC = {cc1(t), cc2(t), cc3(t)}. First, a pair of td1.pc and cc1(t) is selected. The distance between them is smaller than that between others, and the angle is less than 180° and smaller than that between others. Thus, the center coordinate of td1.PC and cc1(t) is calculated, and it is appended to td1.Path. In addition, td1.TTL is incremented. In the next step, because TDL = {td2} and CC = {cc2(t), cc3(t)}, a pair of td2.PC and cc2(t) is selected, and the process described above is applied to the pair.


Multiple Human Tracking Using Binary Infrared Sensors.

Miyazaki T, Kasama Y - Sensors (Basel) (2015)

Example of TrackTarget().
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4507662&req=5

sensors-15-13459-f008: Example of TrackTarget().
Mentions: Figure 8 shows an example of the Path Estimation step. Here, TDL = {td1, td2} and CC = {cc1(t), cc2(t), cc3(t)}. First, a pair of td1.pc and cc1(t) is selected. The distance between them is smaller than that between others, and the angle is less than 180° and smaller than that between others. Thus, the center coordinate of td1.PC and cc1(t) is calculated, and it is appended to td1.Path. In addition, td1.TTL is incremented. In the next step, because TDL = {td2} and CC = {cc2(t), cc3(t)}, a pair of td2.PC and cc2(t) is selected, and the process described above is applied to the pair.

Bottom Line: Our algorithm can estimate multiple human movement paths without a priori knowledge of the number of humans in the room.By repeating predictions and estimations of human positions and links from the previous human positions to the estimated ones at each time period, human movement paths can be estimated.Simulation-based evaluation results show that our algorithm can dynamically trace human movement paths.

View Article: PubMed Central - PubMed

Affiliation: The University of Aizu, Aizuwakamatsu, Fukushima 965-8580, Japan. miyazaki@u-aizu.ac.jp.

ABSTRACT
To create a context-aware environment, human locations and movement paths must be considered. In this paper, we propose an algorithm that tracks human movement paths using only binary sensed data obtained by infrared (IR) sensors attached to the ceiling of a room. Our algorithm can estimate multiple human movement paths without a priori knowledge of the number of humans in the room. By repeating predictions and estimations of human positions and links from the previous human positions to the estimated ones at each time period, human movement paths can be estimated. Simulation-based evaluation results show that our algorithm can dynamically trace human movement paths.

No MeSH data available.