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Vehicle Counting using Video Metadata

Hjelm, Sebastian LU and Gustafsson, Mattias LU (2018) In LU-CS-EX 2018-13 EDAM05 20181
Department of Computer Science
Abstract
The current field of object detection and image recognition is huge but not
without complications. Processing large amounts of high resolution videos
needs powerful hardware and also risks breaching the privacy of those who
are recorded. In times of increasing demand for decentralized solutions and
stricter privacy protection regulations being put in place a new approach is
needed.

We present an alternative to traditional object detection in video where we
analyze changes to its metadata over time rather than the content of the video
frames. This approach has several benefits over traditional object detection: it
is incredibly fast, lightweight and protects the privacy of its subjects.

We have trained and evaluated several... (More)
The current field of object detection and image recognition is huge but not
without complications. Processing large amounts of high resolution videos
needs powerful hardware and also risks breaching the privacy of those who
are recorded. In times of increasing demand for decentralized solutions and
stricter privacy protection regulations being put in place a new approach is
needed.

We present an alternative to traditional object detection in video where we
analyze changes to its metadata over time rather than the content of the video
frames. This approach has several benefits over traditional object detection: it
is incredibly fast, lightweight and protects the privacy of its subjects.

We have trained and evaluated several neural network models tasked with
detecting and counting vehicles in various scenes and have achieved accuracies
above 90%. Finally, we take the first steps toward a decentralized solution
running entirely on embedded devices. (Less)
Please use this url to cite or link to this publication:
author
Hjelm, Sebastian LU and Gustafsson, Mattias LU
supervisor
organization
course
EDAM05 20181
year
type
H3 - Professional qualifications (4 Years - )
subject
keywords
Machine learning, Neural networks, Vehicle detection, CNN, Metadata, Bitrate, QP, Fast, Privacy
publication/series
LU-CS-EX 2018-13
report number
LU-CS-EX 2018-13
ISSN
1650-2884
language
English
id
8962789
date added to LUP
2018-12-19 13:38:31
date last changed
2018-12-19 13:38:31
@misc{8962789,
  abstract     = {{The current field of object detection and image recognition is huge but not
without complications. Processing large amounts of high resolution videos
needs powerful hardware and also risks breaching the privacy of those who
are recorded. In times of increasing demand for decentralized solutions and
stricter privacy protection regulations being put in place a new approach is
needed.

We present an alternative to traditional object detection in video where we
analyze changes to its metadata over time rather than the content of the video
frames. This approach has several benefits over traditional object detection: it
is incredibly fast, lightweight and protects the privacy of its subjects.

We have trained and evaluated several neural network models tasked with
detecting and counting vehicles in various scenes and have achieved accuracies
above 90%. Finally, we take the first steps toward a decentralized solution
running entirely on embedded devices.}},
  author       = {{Hjelm, Sebastian and Gustafsson, Mattias}},
  issn         = {{1650-2884}},
  language     = {{eng}},
  note         = {{Student Paper}},
  series       = {{LU-CS-EX 2018-13}},
  title        = {{Vehicle Counting using Video Metadata}},
  year         = {{2018}},
}