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Flow Counting Using Realboosted Multi-sized Window Detectors

Ardö, Håkan LU ; Nilsson, Mikael LU and Berthilsson, Rikard LU (2012) 3rd IEEE International Workshop on Analysis and Retrieval of Tracked Events and Motion in Imagery Streams (ARTEMIS 2012) In Lecture notes in computer science 7585. p.193-202
Abstract
One classic approach to real-time object detection is to use adaboost to a train a set of look up tables of discrete features. By utilizing a discrete feature set, from features such as local binary patterns, efficient classifiers can be designed. However, these classifiers include interpolation operations while scaling the images over various scales. In this work, we propose the use of real valued weak classifiers which are designed on different scales in order to avoid costly interpolations. The use of real valued weak classifiers in combination with the proposed method avoiding interpolation leads to substantially faster detectors compared to baseline detectors. Furthermore, we investigate the speed and detection performance of such... (More)
One classic approach to real-time object detection is to use adaboost to a train a set of look up tables of discrete features. By utilizing a discrete feature set, from features such as local binary patterns, efficient classifiers can be designed. However, these classifiers include interpolation operations while scaling the images over various scales. In this work, we propose the use of real valued weak classifiers which are designed on different scales in order to avoid costly interpolations. The use of real valued weak classifiers in combination with the proposed method avoiding interpolation leads to substantially faster detectors compared to baseline detectors. Furthermore, we investigate the speed and detection performance of such classifiers and their impact on tracking performance. Results indicate that the realboost framework combined with the proposed scaling framework achieves an 80% speed up over adaboost with bilinear interpolation. (Less)
Please use this url to cite or link to this publication:
author
; and
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
host publication
Computer Vision – ECCV 2012. Workshops and Demonstrations : Florence, Italy, October 7-13, 2012, Proceedings, Part III - Florence, Italy, October 7-13, 2012, Proceedings, Part III
series title
Lecture notes in computer science
volume
7585
pages
193 - 202
publisher
Springer
conference name
3rd IEEE International Workshop on Analysis and Retrieval of Tracked Events and Motion in Imagery Streams (ARTEMIS 2012)
conference location
Florence, Italy
conference dates
2012-10-13 - 2012-10-13
external identifiers
  • scopus:84867696956
ISSN
1611-3349
0302-9743
ISBN
978-3-642-33885-4
978-3-642-33884-7
DOI
10.1007/978-3-642-33885-4_20
language
English
LU publication?
yes
id
19103977-c682-46eb-836e-5169a56a62e9
date added to LUP
2017-10-19 13:07:44
date last changed
2024-01-14 07:55:56
@inproceedings{19103977-c682-46eb-836e-5169a56a62e9,
  abstract     = {{One classic approach to real-time object detection is to use adaboost to a train a set of look up tables of discrete features. By utilizing a discrete feature set, from features such as local binary patterns, efficient classifiers can be designed. However, these classifiers include interpolation operations while scaling the images over various scales. In this work, we propose the use of real valued weak classifiers which are designed on different scales in order to avoid costly interpolations. The use of real valued weak classifiers in combination with the proposed method avoiding interpolation leads to substantially faster detectors compared to baseline detectors. Furthermore, we investigate the speed and detection performance of such classifiers and their impact on tracking performance. Results indicate that the realboost framework combined with the proposed scaling framework achieves an 80% speed up over adaboost with bilinear interpolation.}},
  author       = {{Ardö, Håkan and Nilsson, Mikael and Berthilsson, Rikard}},
  booktitle    = {{Computer Vision – ECCV 2012. Workshops and Demonstrations : Florence, Italy, October 7-13, 2012, Proceedings, Part III}},
  isbn         = {{978-3-642-33885-4}},
  issn         = {{1611-3349}},
  language     = {{eng}},
  pages        = {{193--202}},
  publisher    = {{Springer}},
  series       = {{Lecture notes in computer science}},
  title        = {{Flow Counting Using Realboosted Multi-sized Window Detectors}},
  url          = {{http://dx.doi.org/10.1007/978-3-642-33885-4_20}},
  doi          = {{10.1007/978-3-642-33885-4_20}},
  volume       = {{7585}},
  year         = {{2012}},
}