CNN-based single object detection and tracking in videos and its application to drone detection
Abstract This paper presents convolutional neural network (CNN)-based single object detection and tracking algorithms. CNN-based object detection methods are directly applicable to static images, but not to videos. On the other hand, model-free visual object tracking methods cannot detect an object...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Lee, Dong-Hyun [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2020 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer Science+Business Media, LLC, part of Springer Nature 2020 |
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| Übergeordnetes Werk: |
Enthalten in: Multimedia tools and applications - Springer US, 1995, 80(2020), 26-27 vom: 08. Okt., Seite 34237-34248 |
|---|---|
| Übergeordnetes Werk: |
volume:80 ; year:2020 ; number:26-27 ; day:08 ; month:10 ; pages:34237-34248 |
| Links: |
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| DOI / URN: |
10.1007/s11042-020-09924-0 |
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OLC2077424850 |
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| 520 | |a Abstract This paper presents convolutional neural network (CNN)-based single object detection and tracking algorithms. CNN-based object detection methods are directly applicable to static images, but not to videos. On the other hand, model-free visual object tracking methods cannot detect an object until a ground truth bounding box of the target is provided. Moreover, many annotated video datasets of the target object are required to train both the object detectors and visual trackers. In this work, three simple yet effective object detection and tracking algorithms for videos are proposed to efficiently combine a state-of-the-art object detector and visual tracker for circumstances in which only a few static images of the target are available for training. The proposed algorithms are tested using a drone detection task and the experimental results demonstrated their effectiveness. | ||
| 650 | 4 | |a Object detection | |
| 650 | 4 | |a Object tracking | |
| 650 | 4 | |a Convolutional neural network | |
| 650 | 4 | |a Drone detection | |
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10.1007/s11042-020-09924-0 doi (DE-627)OLC2077424850 (DE-He213)s11042-020-09924-0-p DE-627 ger DE-627 rakwb eng 070 004 VZ Lee, Dong-Hyun verfasserin (orcid)0000-0002-9372-3333 aut CNN-based single object detection and tracking in videos and its application to drone detection 2020 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2020 Abstract This paper presents convolutional neural network (CNN)-based single object detection and tracking algorithms. CNN-based object detection methods are directly applicable to static images, but not to videos. On the other hand, model-free visual object tracking methods cannot detect an object until a ground truth bounding box of the target is provided. Moreover, many annotated video datasets of the target object are required to train both the object detectors and visual trackers. In this work, three simple yet effective object detection and tracking algorithms for videos are proposed to efficiently combine a state-of-the-art object detector and visual tracker for circumstances in which only a few static images of the target are available for training. The proposed algorithms are tested using a drone detection task and the experimental results demonstrated their effectiveness. Object detection Object tracking Convolutional neural network Drone detection Enthalten in Multimedia tools and applications Springer US, 1995 80(2020), 26-27 vom: 08. Okt., Seite 34237-34248 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:80 year:2020 number:26-27 day:08 month:10 pages:34237-34248 https://doi.org/10.1007/s11042-020-09924-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW AR 80 2020 26-27 08 10 34237-34248 |
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10.1007/s11042-020-09924-0 doi (DE-627)OLC2077424850 (DE-He213)s11042-020-09924-0-p DE-627 ger DE-627 rakwb eng 070 004 VZ Lee, Dong-Hyun verfasserin (orcid)0000-0002-9372-3333 aut CNN-based single object detection and tracking in videos and its application to drone detection 2020 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2020 Abstract This paper presents convolutional neural network (CNN)-based single object detection and tracking algorithms. CNN-based object detection methods are directly applicable to static images, but not to videos. On the other hand, model-free visual object tracking methods cannot detect an object until a ground truth bounding box of the target is provided. Moreover, many annotated video datasets of the target object are required to train both the object detectors and visual trackers. In this work, three simple yet effective object detection and tracking algorithms for videos are proposed to efficiently combine a state-of-the-art object detector and visual tracker for circumstances in which only a few static images of the target are available for training. The proposed algorithms are tested using a drone detection task and the experimental results demonstrated their effectiveness. Object detection Object tracking Convolutional neural network Drone detection Enthalten in Multimedia tools and applications Springer US, 1995 80(2020), 26-27 vom: 08. Okt., Seite 34237-34248 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:80 year:2020 number:26-27 day:08 month:10 pages:34237-34248 https://doi.org/10.1007/s11042-020-09924-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW AR 80 2020 26-27 08 10 34237-34248 |
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10.1007/s11042-020-09924-0 doi (DE-627)OLC2077424850 (DE-He213)s11042-020-09924-0-p DE-627 ger DE-627 rakwb eng 070 004 VZ Lee, Dong-Hyun verfasserin (orcid)0000-0002-9372-3333 aut CNN-based single object detection and tracking in videos and its application to drone detection 2020 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2020 Abstract This paper presents convolutional neural network (CNN)-based single object detection and tracking algorithms. CNN-based object detection methods are directly applicable to static images, but not to videos. On the other hand, model-free visual object tracking methods cannot detect an object until a ground truth bounding box of the target is provided. Moreover, many annotated video datasets of the target object are required to train both the object detectors and visual trackers. In this work, three simple yet effective object detection and tracking algorithms for videos are proposed to efficiently combine a state-of-the-art object detector and visual tracker for circumstances in which only a few static images of the target are available for training. The proposed algorithms are tested using a drone detection task and the experimental results demonstrated their effectiveness. Object detection Object tracking Convolutional neural network Drone detection Enthalten in Multimedia tools and applications Springer US, 1995 80(2020), 26-27 vom: 08. Okt., Seite 34237-34248 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:80 year:2020 number:26-27 day:08 month:10 pages:34237-34248 https://doi.org/10.1007/s11042-020-09924-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW AR 80 2020 26-27 08 10 34237-34248 |
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10.1007/s11042-020-09924-0 doi (DE-627)OLC2077424850 (DE-He213)s11042-020-09924-0-p DE-627 ger DE-627 rakwb eng 070 004 VZ Lee, Dong-Hyun verfasserin (orcid)0000-0002-9372-3333 aut CNN-based single object detection and tracking in videos and its application to drone detection 2020 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2020 Abstract This paper presents convolutional neural network (CNN)-based single object detection and tracking algorithms. CNN-based object detection methods are directly applicable to static images, but not to videos. On the other hand, model-free visual object tracking methods cannot detect an object until a ground truth bounding box of the target is provided. Moreover, many annotated video datasets of the target object are required to train both the object detectors and visual trackers. In this work, three simple yet effective object detection and tracking algorithms for videos are proposed to efficiently combine a state-of-the-art object detector and visual tracker for circumstances in which only a few static images of the target are available for training. The proposed algorithms are tested using a drone detection task and the experimental results demonstrated their effectiveness. Object detection Object tracking Convolutional neural network Drone detection Enthalten in Multimedia tools and applications Springer US, 1995 80(2020), 26-27 vom: 08. Okt., Seite 34237-34248 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:80 year:2020 number:26-27 day:08 month:10 pages:34237-34248 https://doi.org/10.1007/s11042-020-09924-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW AR 80 2020 26-27 08 10 34237-34248 |
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10.1007/s11042-020-09924-0 doi (DE-627)OLC2077424850 (DE-He213)s11042-020-09924-0-p DE-627 ger DE-627 rakwb eng 070 004 VZ Lee, Dong-Hyun verfasserin (orcid)0000-0002-9372-3333 aut CNN-based single object detection and tracking in videos and its application to drone detection 2020 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2020 Abstract This paper presents convolutional neural network (CNN)-based single object detection and tracking algorithms. CNN-based object detection methods are directly applicable to static images, but not to videos. On the other hand, model-free visual object tracking methods cannot detect an object until a ground truth bounding box of the target is provided. Moreover, many annotated video datasets of the target object are required to train both the object detectors and visual trackers. In this work, three simple yet effective object detection and tracking algorithms for videos are proposed to efficiently combine a state-of-the-art object detector and visual tracker for circumstances in which only a few static images of the target are available for training. The proposed algorithms are tested using a drone detection task and the experimental results demonstrated their effectiveness. Object detection Object tracking Convolutional neural network Drone detection Enthalten in Multimedia tools and applications Springer US, 1995 80(2020), 26-27 vom: 08. Okt., Seite 34237-34248 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:80 year:2020 number:26-27 day:08 month:10 pages:34237-34248 https://doi.org/10.1007/s11042-020-09924-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW AR 80 2020 26-27 08 10 34237-34248 |
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Abstract This paper presents convolutional neural network (CNN)-based single object detection and tracking algorithms. CNN-based object detection methods are directly applicable to static images, but not to videos. On the other hand, model-free visual object tracking methods cannot detect an object until a ground truth bounding box of the target is provided. Moreover, many annotated video datasets of the target object are required to train both the object detectors and visual trackers. In this work, three simple yet effective object detection and tracking algorithms for videos are proposed to efficiently combine a state-of-the-art object detector and visual tracker for circumstances in which only a few static images of the target are available for training. The proposed algorithms are tested using a drone detection task and the experimental results demonstrated their effectiveness. © Springer Science+Business Media, LLC, part of Springer Nature 2020 |
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Abstract This paper presents convolutional neural network (CNN)-based single object detection and tracking algorithms. CNN-based object detection methods are directly applicable to static images, but not to videos. On the other hand, model-free visual object tracking methods cannot detect an object until a ground truth bounding box of the target is provided. Moreover, many annotated video datasets of the target object are required to train both the object detectors and visual trackers. In this work, three simple yet effective object detection and tracking algorithms for videos are proposed to efficiently combine a state-of-the-art object detector and visual tracker for circumstances in which only a few static images of the target are available for training. The proposed algorithms are tested using a drone detection task and the experimental results demonstrated their effectiveness. © Springer Science+Business Media, LLC, part of Springer Nature 2020 |
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