Adding a rigid motion model to foreground detection: application to moving object detection in rivers
| Autor: | Imtiaz Ali, Laure Tougne, Julien Mille |
|---|---|
| Přispěvatelé: | Extraction de Caractéristiques et Identification (imagine), Laboratoire d'InfoRmatique en Image et Systèmes d'information (LIRIS), Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-École Centrale de Lyon (ECL), Université de Lyon-Université Lumière - Lyon 2 (UL2)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Université Lumière - Lyon 2 (UL2), Geometry Processing and Constrained Optimization (M2DisCo) |
| Rok vydání: | 2013 |
| Předmět: |
Foreground detection
Background subtraction Computer science business.industry ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Motion detection 0102 computer and information sciences 02 engineering and technology 01 natural sciences Object detection Object-class detection 010201 computation theory & mathematics Artificial Intelligence Motion estimation 0202 electrical engineering electronic engineering information engineering Structure from motion [INFO]Computer Science [cs] 020201 artificial intelligence & image processing Computer vision Viola–Jones object detection framework Computer Vision and Pattern Recognition Artificial intelligence business |
| Zdroj: | Pattern Analysis and Applications Pattern Analysis and Applications, Springer Verlag, 2014, 3, 17, pp.567-585. ⟨10.1007/s10044-013-0346-6⟩ |
| ISSN: | 1433-755X 1433-7541 |
| DOI: | 10.1007/s10044-013-0346-6 |
| Popis: | International audience; Object detection in a dynamic backgroundis a challenging task in many computer vision applica-tions. In some situations, the motion of objects can bepredicted thanks to its regularity (e.g. vehicle motion,pedestrian motion). In this article, we propose to modelsuch motion knowledge and to use it as additional infor-mation to help in foreground detection. The inclusionof object motion information provides a measure fordistinguishing moving objects from a background thathas similar sizes and brightness levels. This informationis obtained by applying statistical methods on data ob-tained during the training period.When available, priorknowledge can be incorporated into the foreground de-tection process to improve robustness and to decreasefalse detection. We apply this framework to moving ob-ject detection in rivers, one of the situations in whichclassic background subtraction algorithms fail. Our ex-periments show that the incorporation of prior motiondata into background subtraction improves object de-tection. |
| Databáze: | OpenAIRE |
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