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Flow Counting Using...
Flow Counting Using Realboosted Multi-sized Window Detectors
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- Ardö, Håkan (författare)
- Lund University,Lunds universitet,Mathematical Imaging Group,Forskargrupper vid Lunds universitet,Matematik LTH,Matematikcentrum,Institutioner vid LTH,Lunds Tekniska Högskola,Lund University Research Groups,Mathematics (Faculty of Engineering),Centre for Mathematical Sciences,Departments at LTH,Faculty of Engineering, LTH
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- Nilsson, Mikael (författare)
- Lund University,Lunds universitet,Mathematical Imaging Group,Forskargrupper vid Lunds universitet,Matematik LTH,Matematikcentrum,Institutioner vid LTH,Lunds Tekniska Högskola,Lund University Research Groups,Mathematics (Faculty of Engineering),Centre for Mathematical Sciences,Departments at LTH,Faculty of Engineering, LTH
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- Berthilsson, Rikard (författare)
- Lund University,Lunds universitet,Matematik LTH,Matematikcentrum,Institutioner vid LTH,Lunds Tekniska Högskola,Mathematics (Faculty of Engineering),Centre for Mathematical Sciences,Departments at LTH,Faculty of Engineering, LTH,Cognimatics AB
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(creator_code:org_t)
- Berlin, Heidelberg : Springer Berlin Heidelberg, 2012
- 2012
- Engelska.
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Ingår i: 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. - Berlin, Heidelberg : Springer Berlin Heidelberg. - 1611-3349 .- 0302-9743. - 9783642338847 - 9783642338854 ; 7585, s. 193-202
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Abstract
Ämnesord
Stäng
- 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.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datorseende och robotik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Vision and Robotics (hsv//eng)
Publikations- och innehållstyp
- kon (ämneskategori)
- ref (ämneskategori)
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