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Detecting pedestrians intending to enter a crosswalk using a HMM tracker and a novel predictor

Hasselberg, Emanuel (2013) In Master's Theses in Mathematical Sciences FMA820 20131
Mathematics (Faculty of Engineering)
Abstract
There is a demand for a more fluent and more efficient traffic. This can be achieved through more intelligent traffic light control. This thesis presents theory and an application in which people are tracked and their intentions to cross a crosswalk is predicted with a novel prediction algorithm based on Markov theory. The background segmentation and tracking algorithms was based on already known cross-correlation and HMM-methods.
Based on the relatively small amount of training data the result for the novel predictor detecting persons "entering the crosswalk" for two different setups, a straight and a four way crossing, is 70% and 55% true positives with 5% and 2% false positives. For detection of someone that is "not entering the... (More)
There is a demand for a more fluent and more efficient traffic. This can be achieved through more intelligent traffic light control. This thesis presents theory and an application in which people are tracked and their intentions to cross a crosswalk is predicted with a novel prediction algorithm based on Markov theory. The background segmentation and tracking algorithms was based on already known cross-correlation and HMM-methods.
Based on the relatively small amount of training data the result for the novel predictor detecting persons "entering the crosswalk" for two different setups, a straight and a four way crossing, is 70% and 55% true positives with 5% and 2% false positives. For detection of someone that is "not entering the crosswalk" when there is a person in the area is 90% and 85% true positives with 15% and 25% false positive.
The results achieved are good enough as a proof of concept that the theories are worth investigating further for these kind of applications. However, a lot of work would still be required before this is robust enough to be in a real traffic application. (Less)
Please use this url to cite or link to this publication:
author
Hasselberg, Emanuel
supervisor
organization
course
FMA820 20131
year
type
H3 - Professional qualifications (4 Years - )
subject
publication/series
Master's Theses in Mathematical Sciences
report number
LUTFMA-3245-2013
ISSN
1404-6342
other publication id
2013:E2
language
English
id
3801537
date added to LUP
2013-05-29 14:36:55
date last changed
2013-05-29 14:40:36
@misc{3801537,
  abstract     = {There is a demand for a more fluent and more efficient traffic. This can be achieved through more intelligent traffic light control. This thesis presents theory and an application in which people are tracked and their intentions to cross a crosswalk is predicted with a novel prediction algorithm based on Markov theory. The background segmentation and tracking algorithms was based on already known cross-correlation and HMM-methods.
Based on the relatively small amount of training data the result for the novel predictor detecting persons "entering the crosswalk" for two different setups, a straight and a four way crossing, is 70% and 55% true positives with 5% and 2% false positives. For detection of someone that is "not entering the crosswalk" when there is a person in the area is 90% and 85% true positives with 15% and 25% false positive. 
The results achieved are good enough as a proof of concept that the theories are worth investigating further for these kind of applications. However, a lot of work would still be required before this is robust enough to be in a real traffic application.},
  author       = {Hasselberg, Emanuel},
  issn         = {1404-6342},
  language     = {eng},
  note         = {Student Paper},
  series       = {Master's Theses in Mathematical Sciences},
  title        = {Detecting pedestrians intending to enter a crosswalk using a HMM tracker and a novel predictor},
  year         = {2013},
}