Fraunhofer-Gesellschaft

Publica

Hier finden Sie wissenschaftliche Publikationen aus den Fraunhofer-Instituten.

Robust and fast detection of moving vehicles in aerial videos using sliding windows

 
: Teutsch, Michael; Krüger, Wolfgang

:
Preprint urn:nbn:de:0011-n-3643810 (2.1 MByte PDF)
MD5 Fingerprint: 0969753c03340cf0d394f8112d25cf51
© IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Erstellt am: 17.11.2015


Institute of Electrical and Electronics Engineers -IEEE-:
28th IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2015. Proceedings : 7-12 June 2015, Boston, USA
Piscataway, NJ: IEEE, 2015
ISBN: 978-1-4673-6759-2
ISBN: 978-1-4673-6760-8
S.26-34
Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) <28, 2015, Boston/Mass.>
Englisch
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IOSB ()

Abstract
The detection of vehicles driving on busy urban streets in videos acquired by airborne cameras is challenging due to the large distance between camera and vehicles, simultaneous vehicle and camera motion, shadows, or low contrast due to weak illumination. However, it is an important processing step for applications such as automatic traffic monitoring, detection of abnormal behaviour, border protection, or surveillance of restricted areas. In contrast to commonly applied object segmentation methods based on background subtraction or frame differencing, we detect moving vehicles using the combination of a track-before-detect (TBD) approach and machine learning: an AdaBoost classifier learns the appearance of vehicles in low resolution and is applied within a sliding window algorithm to detect vehicles inside a region of interest determined by the TBD approach. Our main contribution lies in the identification, optimization, and evaluation of the most important parameters to achieve both high detection rates and real-time processing.

: http://publica.fraunhofer.de/dokumente/N-364381.html