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Proceedings Paper

Combining high-speed SVM learning with CNN feature encoding for real-time target recognition in high-definition video for ISR missions
Author(s): Christine Kroll; Monika von der Werth; Holger Leuck; Christoph Stahl; Klaus Schertler
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Paper Abstract

For Intelligence, Surveillance, Reconnaissance (ISR) missions of manned and unmanned air systems typical electrooptical payloads provide high-definition video data which has to be exploited with respect to relevant ground targets in real-time by automatic/assisted target recognition software. Airbus Defence and Space is developing required technologies for real-time sensor exploitation since years and has combined the latest advances of Deep Convolutional Neural Networks (CNN) with a proprietary high-speed Support Vector Machine (SVM) learning method into a powerful object recognition system with impressive results on relevant high-definition video scenes compared to conventional target recognition approaches. This paper describes the principal requirements for real-time target recognition in high-definition video for ISR missions and the Airbus approach of combining an invariant feature extraction using pre-trained CNNs and the high-speed training and classification ability of a novel frequency-domain SVM training method. The frequency-domain approach allows for a highly optimized implementation for General Purpose Computation on a Graphics Processing Unit (GPGPU) and also an efficient training of large training samples. The selected CNN which is pre-trained only once on domain-extrinsic data reveals a highly invariant feature extraction. This allows for a significantly reduced adaptation and training of the target recognition method for new target classes and mission scenarios. A comprehensive training and test dataset was defined and prepared using relevant high-definition airborne video sequences. The assessment concept is explained and performance results are given using the established precision-recall diagrams, average precision and runtime figures on representative test data. A comparison to legacy target recognition approaches shows the impressive performance increase by the proposed CNN+SVM machine-learning approach and the capability of real-time high-definition video exploitation.

Paper Details

Date Published: 1 May 2017
PDF: 10 pages
Proc. SPIE 10202, Automatic Target Recognition XXVII, 1020208 (1 May 2017); doi: 10.1117/12.2262064
Show Author Affiliations
Christine Kroll, Airbus Defence and Space (Germany)
Monika von der Werth, Airbus Defence and Space (Germany)
Holger Leuck, Airbus Defence and Space (Germany)
Christoph Stahl, Airbus Defence and Space (Germany)
Klaus Schertler, Airbus Group Innovations (Germany)


Published in SPIE Proceedings Vol. 10202:
Automatic Target Recognition XXVII
Firooz A. Sadjadi; Abhijit Mahalanobis, Editor(s)

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