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Evaluation of object segmentation to improve moving vehicle detection in aerial videos

: Teutsch, Michael; Krüger, Wolfgang; Beyerer, Jürgen

Postprint urn:nbn:de:0011-n-3323844 (1.5 MByte PDF)
MD5 Fingerprint: dd98b8e1cde41987e151afef696c46a3
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Erstellt am: 24.3.2015

Ko. Hanseok (Ed.) ; Institute of Electrical and Electronics Engineers -IEEE-; IEEE Signal Processing Society:
11th IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2014 : Seoul, South Korea, 26 - 29 August 2014
Piscataway, NJ: IEEE, 2014
ISBN: 978-1-4799-4871-0
ISBN: 978-1-4799-4870-3
International Conference on Advanced Video and Signal Based Surveillance (AVSS) <11, 2014, Seoul>
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IOSB ()

Moving objects play a key role for gaining scene understanding in aerial surveillance tasks. The detection of moving vehicles can be challenging due to high object distance, simultaneous object and camera motion, shadows, or weak contrast. In scenarios where vehicles are driving on busy urban streets, this is even more challenging due to possible merged detections. In this paper, a video processing chain is proposed for moving vehicle detection and segmentation. The fundament for detecting motion which is independent of the camera motion is tracking of local image features such as Harris corners. Independently moving features are clustered. Since motion clusters are prone to merge similarly moving objects, we evaluate various object segmentation approaches based on contour extraction, blob extraction, or machine learning to handle such effects. We propose to use a local sliding window approach with Integral Channel Features (ICF) and AdaBoost classifier.