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  Active Structured Learning for High-Speed Object Detection

Lampert, C., & Peters, J. (2009). Active Structured Learning for High-Speed Object Detection. Pattern Recognition: 31st DAGM Symposium, 221-231.

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 Urheber:
Lampert, CH1, 2, Autor           
Peters, J1, 3, Autor           
Denzler, Herausgeber
J., Herausgeber
Notni, G., Herausgeber
Süsse, H., Herausgeber
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Dept. Empirical Inference, Max Planck Institute for Intelligent System, Max Planck Society, ou_1497647              
3Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society, ou_1497647              

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 Zusammenfassung: High-speed smooth and accurate visual tracking of objects in arbitrary, unstructured environments is essential for robotics and human motion analysis. However, building a system that can adapt to arbitrary objects and a wide range of lighting conditions is a challenging problem, especially if hard real-time constraints apply like in robotics scenarios. In this work, we introduce a method for learning a discriminative object tracking system based on the recent structured regression framework for object localization. Using a kernel function that allows fast evaluation on the GPU, the resulting system can process video streams at speed of 100 frames per second or more. Consecutive frames in high speed video sequences are typically very redundant, and for training an object detection system, it is sufficient to have training labels from only a subset of all images. We propose an active learning method that select training examples in a data-driven way, thereby minimizing the required number of training labeling. Experiments on realistic data show that the active learning is superior to previously used methods for dataset subsampling for this task.

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 Datum: 2009-09
 Publikationsstatus: Erschienen
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 Ort, Verlag, Ausgabe: -
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 Art des Abschluß: -

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Titel: 31st Annual Symposium of the German Association for Pattern Recognition
Veranstaltungsort: Jena, Germany
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Titel: Pattern Recognition: 31st DAGM Symposium
Genre der Quelle: Zeitschrift
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Ort, Verlag, Ausgabe: Berlin, Germany : Springer
Seiten: - Band / Heft: - Artikelnummer: - Start- / Endseite: 221 - 231 Identifikator: -