Zeng, Dongdong ; Zhu, Ming ; Kuijper, Arjan (2019)
Combining background subtraction algorithms with convolutional neural network.
In: Journal of Electronic Imaging, 28 (01)
doi: 10.1117/1.JEI.28.1.013011
Artikel, Bibliographie
Kurzbeschreibung (Abstract)
Accurate and fast extraction of foreground objects is a key prerequisite for a wide range of computer vision applications, such as object tracking and recognition. Thus, many background subtraction (BGS) methods for foreground object detection have been proposed in recent decades. However, this is still regarded as a tough problem due to a variety of challenges, such as illumination variations, camera jitter, dynamic backgrounds, and shadows. Currently, there is no single method that can handle all the challenges in a robust way. We try to solve this problem from a perspective of combining different state-of-the-art BGS algorithms to create a more robust and more advanced foreground detection algorithm. More specifically, an encoder–decoder fully convolutional neural network architecture is adapted and trained to automatically learn how to leverage the characteristics of different algorithms to fuse the results produced by different BGS algorithms and produce a more precise result. Comprehensive experiments evaluated on the CDnet 2014 dataset demonstrate that the proposed method outperforms all the considered single BGS algorithms. We show that our solution is more efficient than other BGS combination strategies.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2019 |
| Autor(en): | Zeng, Dongdong ; Zhu, Ming ; Kuijper, Arjan |
| Art des Eintrags: | Bibliographie |
| Titel: | Combining background subtraction algorithms with convolutional neural network |
| Sprache: | Englisch |
| Publikationsjahr: | 2019 |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | Journal of Electronic Imaging |
| Jahrgang/Volume einer Zeitschrift: | 28 |
| (Heft-)Nummer: | 01 |
| DOI: | 10.1117/1.JEI.28.1.013011 |
| URL / URN: | https://doi.org/10.1117/1.JEI.28.1.013011 |
| Kurzbeschreibung (Abstract): | Accurate and fast extraction of foreground objects is a key prerequisite for a wide range of computer vision applications, such as object tracking and recognition. Thus, many background subtraction (BGS) methods for foreground object detection have been proposed in recent decades. However, this is still regarded as a tough problem due to a variety of challenges, such as illumination variations, camera jitter, dynamic backgrounds, and shadows. Currently, there is no single method that can handle all the challenges in a robust way. We try to solve this problem from a perspective of combining different state-of-the-art BGS algorithms to create a more robust and more advanced foreground detection algorithm. More specifically, an encoder–decoder fully convolutional neural network architecture is adapted and trained to automatically learn how to leverage the characteristics of different algorithms to fuse the results produced by different BGS algorithms and produce a more precise result. Comprehensive experiments evaluated on the CDnet 2014 dataset demonstrate that the proposed method outperforms all the considered single BGS algorithms. We show that our solution is more efficient than other BGS combination strategies. |
| Freie Schlagworte: | Object detection, Convolutional Neural Networks (CNN) |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Mathematisches und angewandtes Visual Computing |
| Hinterlegungsdatum: | 26 Jun 2019 07:43 |
| Letzte Änderung: | 26 Jun 2019 07:43 |
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