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Analyzing low light adaption methods for pre-trained cascade of boosted MB-LBP classifiers

Jarlskog, Måns LU (2016) In Master's Theses in Mathematical Sciences FMA820 20151
Mathematics (Faculty of Engineering)
Abstract
In applications of real time gesture detection on devices which have cameras and limited power supply it is important to consider both robustness and maintaining a low power consumption. A well explored approach is the use of cascades of boosted classifiers. As retraining is a very cumbersome process, online environment adaption would be an appealing approach to counter situations where the boosted cascade detector underperforms.
The purpose of this thesis is to investigate ways to adapt a pre-trained boosted cascade detector, with regression trees as weak classifiers, to an elusive environment in test-time.
The approach is unsupervised collection of data in real time on highly probable false negatives coming from temporal series of... (More)
In applications of real time gesture detection on devices which have cameras and limited power supply it is important to consider both robustness and maintaining a low power consumption. A well explored approach is the use of cascades of boosted classifiers. As retraining is a very cumbersome process, online environment adaption would be an appealing approach to counter situations where the boosted cascade detector underperforms.
The purpose of this thesis is to investigate ways to adapt a pre-trained boosted cascade detector, with regression trees as weak classifiers, to an elusive environment in test-time.
The approach is unsupervised collection of data in real time on highly probable false negatives coming from temporal series of frames. The data is then used to find features associated with missed detections and increase their influence on the classifier while keeping the unwanted impact bounded. Adaption is accomplished by reclassifying any new low confidence data with a set of adapted leaves.
In this thesis, adaption to a low light environment is investigated. Methods to unsupervised collect data and output adapted leaf values are presented. The results suggest convergence of the increased detection rates up to 13%, with respect to both the number of target stages and the size of the reclassification area, can be achieved with a few percent increase of feature evaluations.
In order to draw further conclusions, about the general performance as well as the environment biased performance, more data is needed together with appropriate tools for analyzing results, such as ROC-curves. (Less)
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author
Jarlskog, Måns LU
supervisor
organization
course
FMA820 20151
year
type
H2 - Master's Degree (Two Years)
subject
publication/series
Master's Theses in Mathematical Sciences
report number
LUTFMA-3289-2016
ISSN
1404-6342
other publication id
2016:E8
language
English
id
8873017
date added to LUP
2016-08-25 15:31:05
date last changed
2016-08-25 15:31:05
@misc{8873017,
  abstract     = {In applications of real time gesture detection on devices which have cameras and limited power supply it is important to consider both robustness and maintaining a low power consumption. A well explored approach is the use of cascades of boosted classifiers. As retraining is a very cumbersome process, online environment adaption would be an appealing approach to counter situations where the boosted cascade detector underperforms.
The purpose of this thesis is to investigate ways to adapt a pre-trained boosted cascade detector, with regression trees as weak classifiers, to an elusive environment in test-time.
The approach is unsupervised collection of data in real time on highly probable false negatives coming from temporal series of frames. The data is then used to find features associated with missed detections and increase their influence on the classifier while keeping the unwanted impact bounded. Adaption is accomplished by reclassifying any new low confidence data with a set of adapted leaves.
In this thesis, adaption to a low light environment is investigated. Methods to unsupervised collect data and output adapted leaf values are presented. The results suggest convergence of the increased detection rates up to 13%, with respect to both the number of target stages and the size of the reclassification area, can be achieved with a few percent increase of feature evaluations.
In order to draw further conclusions, about the general performance as well as the environment biased performance, more data is needed together with appropriate tools for analyzing results, such as ROC-curves.},
  author       = {Jarlskog, Måns},
  issn         = {1404-6342},
  language     = {eng},
  note         = {Student Paper},
  series       = {Master's Theses in Mathematical Sciences},
  title        = {Analyzing low light adaption methods for pre-trained cascade of boosted MB-LBP classifiers},
  year         = {2016},
}