Boosting based object detection using a geometric model

Conference contribution
(Conference Contribution)


Publication Details

Author(s): Quast K, Seeger C, Trivedi M, Kaup A
Publication year: 2011
Pages range: 3630-3633
ISBN: 9781457713033
Language: English


Abstract


In this paper we present a new method for automatic object detection in images and video sequences. As a classifier the popular AdaBoost algorithm is used, that combines several weak classifiers into one strong classifier. To create a detector based on this classifier, the weak classifiers are set into relation during boosting by using a geometric model. All votes of the weak detectors are evaluated in a voting space. The voting space allows a detection with combinations of different object features. We trained and tested the proposed method with SIFT and kAS features and combinations of these. The learned detector is then used to localize objects in images and video sequences. The performance of the algorithm is examined based on selected image data. © 2011 IEEE.


FAU Authors / FAU Editors

Kaup, André Prof. Dr.-Ing.
Lehrstuhl für Multimediakommunikation und Signalverarbeitung
Quast, Katharina Dr.-Ing.
Lehrstuhl für Multimediakommunikation und Signalverarbeitung
Seeger, Christoph
Lehrstuhl für Informatik 5 (Mustererkennung)


External institutions
University of California, San Diego


How to cite

APA:
Quast, K., Seeger, C., Trivedi, M., & Kaup, A. (2011). Boosting based object detection using a geometric model. In Proceedings of the 2011 18th IEEE International Conference on Image Processing, ICIP 2011 (pp. 3630-3633). Brussels, BE.

MLA:
Quast, Katharina, et al. "Boosting based object detection using a geometric model." Proceedings of the 2011 18th IEEE International Conference on Image Processing, ICIP 2011, Brussels 2011. 3630-3633.

BibTeX: 

Last updated on 2019-28-04 at 15:23