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[RLF16] Contextual discriminatively trained model mixture for object detection in aerial images
Conférence Internationale avec comité de lecture : International Conference on Big Data from Space (BiDS'16), March 2016, pp.129-132, Spain, (DOI: 10.2788/854791)Mots clés: High resolution aerial images; object detection; context models
Résumé:
In this work we propose a new method for vehicle detection in very high resolution aerial images. Our model is based on a mixture of filters which capture the visual appearance of the object of interest. Each filter is discriminatively trained in order to model the implicit subcategories in the training dataset. We use an iterative hard-negative mining procedure to focus the detector on difficult samples. We model interactions between objects using contextual information in order to improve the precision of object detectors. We assess our ap-
proach on several large datasets and show it tackles efficiently major problems in remote sensing such as orientation change
and data size.
Equipe:
vertigo
Collaboration:
ONERA - DTIM
BibTeX
| @inproceedings { | |||
| RLF16, | |||
| title | = | "{Contextual discriminatively trained model mixture for object detection in aerial images}", | |
| author | = | " H. Randrianarivo and B. Le Saux and M. Ferecatu and M. Crucianu ", | |
| booktitle | = | "{International Conference on Big Data from Space (BiDS'16)}", | |
| year | = | 2016, | |
| month | = | "March", | |
| pages | = | "129-132", | |
| address | = | " Spain", | |
| doi | = | "10.2788/854791", | |
| } | |||
