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Heterogeneous Sensor Fusion: Verification and Optimization

Lindelöf Bilski, Henrik LU (2017) In Master's Theses in Mathematical Sciences FMA820 20162
Mathematics (Faculty of Engineering)
Abstract
The project analyzes, implements, and tests different types of heterogeneous sensor fusion, and comparatively checks them against a ground truth. Four different cases are tested, only image data, only non-image data, sequential fusion of data, and parallel fusion of data.

The tracking is done with a probabilistic data association filter, which is a variation of the standard Kalman filter. The metrics are the Clear MOT Metrics. Steepest descent optimization is performed on the parameters to improve result.
Please use this url to cite or link to this publication:
author
Lindelöf Bilski, Henrik LU
supervisor
organization
course
FMA820 20162
year
type
H2 - Master's Degree (Two Years)
subject
keywords
tracking, sensor fusion, parameter optimization, image analysis, kalman filter, pdaf
publication/series
Master's Theses in Mathematical Sciences
report number
LUTFMA-3312-2017
ISSN
1404-6342
other publication id
2017:E9
language
English
id
8904959
date added to LUP
2017-04-03 15:28:18
date last changed
2018-10-11 16:21:06
@misc{8904959,
  abstract     = {The project analyzes, implements, and tests different types of heterogeneous sensor fusion, and comparatively checks them against a ground truth. Four different cases are tested, only image data, only non-image data, sequential fusion of data, and parallel fusion of data.

The tracking is done with a probabilistic data association filter, which is a variation of the standard Kalman filter. The metrics are the Clear MOT Metrics. Steepest descent optimization is performed on the parameters to improve result.},
  author       = {Lindelöf Bilski, Henrik},
  issn         = {1404-6342},
  keyword      = {tracking,sensor fusion,parameter optimization,image analysis,kalman filter,pdaf},
  language     = {eng},
  note         = {Student Paper},
  series       = {Master's Theses in Mathematical Sciences},
  title        = {Heterogeneous Sensor Fusion: Verification and Optimization},
  year         = {2017},
}