| Title |
Pedestrian counting with grid-based binary sensors based on Monte Carlo method
|
|---|---|
| Published in |
SpringerPlus, June 2014
|
| DOI | 10.1186/2193-1801-3-299 |
| Pubmed ID | |
| Authors |
Shuto Fujii, Yoshiaki Taniguchi, Go Hasegawa, Morito Matsuoka |
| Abstract |
In this paper, we propose a method for estimating the number of pedestrians walking in opposite directions, as in cases of a shopping street or a sidewalk in a downtown area. The proposed method utilizes a compound-eye sensor that is constructed by placing two binary sensors for the pedestrians' movement direction and multiple binary sensors for the vertical direction of the pedestrians' movement direction. A number of Monte Carlo simulations about the movement of pedestrians are conducted, and the output history of the compound-eye sensor is obtained in each simulation. The simulation scenario with a small difference of the output history of the compound-eye sensor is selected to estimate the number of pedestrians. Evaluation results show that in the field whose width is 8 [m] the relative error in the proposed method is the smallest by using 2×8 binary sensors. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Japan | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 14 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Japan | 1 | 7% |
| Italy | 1 | 7% |
| Unknown | 12 | 86% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Bachelor | 3 | 21% |
| Student > Master | 3 | 21% |
| Student > Postgraduate | 2 | 14% |
| Professor | 1 | 7% |
| Professor > Associate Professor | 1 | 7% |
| Other | 1 | 7% |
| Unknown | 3 | 21% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 4 | 29% |
| Engineering | 4 | 29% |
| Social Sciences | 1 | 7% |
| Psychology | 1 | 7% |
| Unknown | 4 | 29% |
Attention Score in Context
This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 01 July 2014.
All research outputs
#13,176,689
of 22,757,090 outputs
Outputs from SpringerPlus
#653
of 1,852 outputs
Outputs of similar age
#108,719
of 228,185 outputs
Outputs of similar age from SpringerPlus
#25
of 72 outputs
Altmetric has tracked 22,757,090 research outputs across all sources so far. This one is in the 41st percentile – i.e., 41% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,852 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.7. This one has gotten more attention than average, scoring higher than 63% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 228,185 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 51% of its contemporaries.
We're also able to compare this research output to 72 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 58% of its contemporaries.