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Analysing pedestrian movement with the Flowity software

Karlelid, Johan LU (2020) In LUTVDG/TVBB VBRM01 20192
Division of Fire Safety Engineering
Abstract
This thesis surveyed existing technologies and research on pedestrian detection systems using video as
input. The purpose of the study was to investigate what such a software would need to produce to be
relevant for the fire safety engineer in outdoor circulation- or egress applications. The thesis tested a
newly developed detection software called Flowity. The software, developed by ÅF Digital Solutions
AB (a subsidiary to AFRY AB) utilizes machine learning and artificial intelligence algorithms to
identify and detect objects and pedestrians. The objective was to establish a list of factors that the
Flowity software should be able to extract and how, if to be useful for the fire safety engineer. The
objective was also to conduct a... (More)
This thesis surveyed existing technologies and research on pedestrian detection systems using video as
input. The purpose of the study was to investigate what such a software would need to produce to be
relevant for the fire safety engineer in outdoor circulation- or egress applications. The thesis tested a
newly developed detection software called Flowity. The software, developed by ÅF Digital Solutions
AB (a subsidiary to AFRY AB) utilizes machine learning and artificial intelligence algorithms to
identify and detect objects and pedestrians. The objective was to establish a list of factors that the
Flowity software should be able to extract and how, if to be useful for the fire safety engineer. The
objective was also to conduct a case-study test of the software on a video of pedestrian movement and
to evaluate/compare the capabilities to accurately identify and quantify pedestrian movement with the
Flowity software and do a comparison to manually collected data. Results from the study show that
the Flowity software could identify people and automatically presented them as detected pedestrians
with a detection accuracy of 70 %. This applies for this case study, which was an outdoor highresolution video recording containing 576 pedestrians, with the camera placed 4 meters above the
walking area. The software managed to provide data on movement patterns, route choices of detected
pedestrians as well as measuring movement speeds, flows and densities at different sections. The
maximum global people density measured with the software was 0,13 persons/m2 and the maximum
local density was 3 persons/m2. When comparing the manually and software measured flows and
densities, there was no statistically significant difference between the measurement methods.
However, a comparison between manually and software measured movement speeds showed a
statistically significant difference between the measurement methods. A 14,2 % higher average flow
was measured with the manual counting and a 15,1 % higher average global denisity. The software
measured a 32 % higher average speed than what was manually measured. Uncertainties connected to
the manual measurements and unknown influence of factors on detection performance might have
impacted the results. (Less)
Please use this url to cite or link to this publication:
author
Karlelid, Johan LU
supervisor
organization
course
VBRM01 20192
year
type
M2 - Bachelor Degree
subject
keywords
Detection, Pedestrian detection, Egress, Computer vision, Video analysis, Speed, Flow, Density, Deep learning, Neural network, Fire safety, Evacuation, Engineering, Lund university, LTH
publication/series
LUTVDG/TVBB
report number
5605
other publication id
LUTVDG/TVBB--5605--SE
language
English
id
9005839
date added to LUP
2020-02-28 15:51:48
date last changed
2020-02-28 15:51:48
@misc{9005839,
  abstract     = {{This thesis surveyed existing technologies and research on pedestrian detection systems using video as
input. The purpose of the study was to investigate what such a software would need to produce to be
relevant for the fire safety engineer in outdoor circulation- or egress applications. The thesis tested a
newly developed detection software called Flowity. The software, developed by ÅF Digital Solutions
AB (a subsidiary to AFRY AB) utilizes machine learning and artificial intelligence algorithms to
identify and detect objects and pedestrians. The objective was to establish a list of factors that the
Flowity software should be able to extract and how, if to be useful for the fire safety engineer. The
objective was also to conduct a case-study test of the software on a video of pedestrian movement and
to evaluate/compare the capabilities to accurately identify and quantify pedestrian movement with the
Flowity software and do a comparison to manually collected data. Results from the study show that
the Flowity software could identify people and automatically presented them as detected pedestrians
with a detection accuracy of 70 %. This applies for this case study, which was an outdoor highresolution video recording containing 576 pedestrians, with the camera placed 4 meters above the
walking area. The software managed to provide data on movement patterns, route choices of detected
pedestrians as well as measuring movement speeds, flows and densities at different sections. The
maximum global people density measured with the software was 0,13 persons/m2 and the maximum
local density was 3 persons/m2. When comparing the manually and software measured flows and
densities, there was no statistically significant difference between the measurement methods.
However, a comparison between manually and software measured movement speeds showed a
statistically significant difference between the measurement methods. A 14,2 % higher average flow
was measured with the manual counting and a 15,1 % higher average global denisity. The software
measured a 32 % higher average speed than what was manually measured. Uncertainties connected to
the manual measurements and unknown influence of factors on detection performance might have
impacted the results.}},
  author       = {{Karlelid, Johan}},
  language     = {{eng}},
  note         = {{Student Paper}},
  series       = {{LUTVDG/TVBB}},
  title        = {{Analysing pedestrian movement with the Flowity software}},
  year         = {{2020}},
}