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Semantic labeling for improved vehicle detection in aerial imagery

 
: Sommer, L.; Nie, K.; Schumann, A.; Schuchert, Tobias; Beyerer, Jürgen

:
Fulltext urn:nbn:de:0011-n-4819034 (605 KByte PDF)
MD5 Fingerprint: 30f6758fe4a3f23cd911b4e1c10e94b9
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Created on: 8.2.2018


Institute of Electrical and Electronics Engineers -IEEE-:
14th IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2017 : August 29, 2017-September 1, 2017, Lecce
Piscataway, NJ: IEEE, 2017
ISBN: 978-1-5386-2939-0
ISBN: 978-1-5386-2940-6 (Print)
pp.305-310
International Conference on Advanced Video and Signal Based Surveillance (AVSS) <14, 2017, Lecce>
English
Conference Paper, Electronic Publication
Fraunhofer IOSB ()
contextual information; conventional detection; detection framework; detection performance; false positive detection; ground sampling distance; semantic labeling; traffic monitoring

Abstract
Growing cities and increasing traffic densities result in an increased demand for applications such as traffic monitoring, traffic analysis, and support of rescue work. These applications share the need for accurate detection of relevant vehicles, e.g. in aerial imagery. Recently, the application of deep learning based detection frameworks like Faster R-CNN clearly outperformed conventional detection methods for vehicle detection in aerial images. In this paper, we propose a detection framework that fuses Faster R-CNN and semantic labeling to integrate contextual information. We achieve an improved detection performance by decreasing the number of false positive detections while the number of candidate regions to classify is reduced. To demonstrate the generalization of our approach, we evaluate our detection framework for various ground sampling distances on a publicly available dataset.

: http://publica.fraunhofer.de/documents/N-481903.html