Advanced

Sensor Fusion for Dynamic Privacy Masking

Pendse, Mikael LU and Ben Hamida, Änis LU (2013) In Master's Theses in Mathematical Sciences FMA820 20131
Mathematics (Faculty of Engineering)
Abstract
This master thesis investigates the possibility of combining a regular visual surveillance camera with a thermal infrared surveillance camera monitoring the same view of a scene to detect and classify heat radiating objects such as human beings. A method is presented for the application of privacy masking of a window while detecting heat radiating objects between the cameras and the window and preventing them from being masked out. The proposed method is based on determining if an object is present in both the visual and the IR frame. Registration of image pairs is done using thin plate spline interpolation. Foreground segmentation is done using a Mixture of Gaussians method. The proposed method looks at connected components from the... (More)
This master thesis investigates the possibility of combining a regular visual surveillance camera with a thermal infrared surveillance camera monitoring the same view of a scene to detect and classify heat radiating objects such as human beings. A method is presented for the application of privacy masking of a window while detecting heat radiating objects between the cameras and the window and preventing them from being masked out. The proposed method is based on determining if an object is present in both the visual and the IR frame. Registration of image pairs is done using thin plate spline interpolation. Foreground segmentation is done using a Mixture of Gaussians method. The proposed method looks at connected components from the foreground segmentation and for each component determines if it should be excluded from the mask. Classification is done by thresholding scores obtained by matching features in corresponding IR-visual frame pairs. Three measures for classifying heat radiating objects and reflections in an IR image are also proposed. The classification routine, when combined with the proposed measures, achieves a 98.9% true positive rate and a true negative rate of 99.7%. (Less)
Please use this url to cite or link to this publication:
author
Pendse, Mikael LU and Ben Hamida, Änis LU
supervisor
organization
course
FMA820 20131
year
type
H2 - Master's Degree (Two Years)
subject
keywords
sensor fusion, image analysis, computer vision, infrared, camera, surveillance
publication/series
Master's Theses in Mathematical Sciences
report number
LUTFMA-3253-2013
ISSN
1404-6342
other publication id
2013:E45
language
English
id
4024136
alternative location
http://www2.maths.lth.se/vision/education/pages/HamidaPendse/index.html
date added to LUP
2014-07-04 17:31:05
date last changed
2014-07-04 17:31:05
@misc{4024136,
  abstract     = {This master thesis investigates the possibility of combining a regular visual surveillance camera with a thermal infrared surveillance camera monitoring the same view of a scene to detect and classify heat radiating objects such as human beings. A method is presented for the application of privacy masking of a window while detecting heat radiating objects between the cameras and the window and preventing them from being masked out. The proposed method is based on determining if an object is present in both the visual and the IR frame. Registration of image pairs is done using thin plate spline interpolation. Foreground segmentation is done using a Mixture of Gaussians method. The proposed method looks at connected components from the foreground segmentation and for each component determines if it should be excluded from the mask. Classification is done by thresholding scores obtained by matching features in corresponding IR-visual frame pairs. Three measures for classifying heat radiating objects and reflections in an IR image are also proposed. The classification routine, when combined with the proposed measures, achieves a 98.9% true positive rate and a true negative rate of 99.7%.},
  author       = {Pendse, Mikael and Ben Hamida, Änis},
  issn         = {1404-6342},
  keyword      = {sensor fusion,image analysis,computer vision,infrared,camera,surveillance},
  language     = {eng},
  note         = {Student Paper},
  series       = {Master's Theses in Mathematical Sciences},
  title        = {Sensor Fusion for Dynamic Privacy Masking},
  year         = {2013},
}