Scale-adaptive tracking based on perceptual hash and correlation filter
Abstract In the research on computer vision, object tracking has encountered various challenges, such as occlusion and scale variation. In recent years, tracking-by-detection methods have performed competitively. Some of these methods have focused on solving the problem of scale variation. Regardles...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Huang, Wei [verfasserIn] |
|---|
| Format: |
Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2018 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer Science+Business Media, LLC, part of Springer Nature 2018 |
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| Übergeordnetes Werk: |
Enthalten in: Multimedia tools and applications - Springer US, 1995, 78(2018), 12 vom: 12. Dez., Seite 16011-16032 |
|---|---|
| Übergeordnetes Werk: |
volume:78 ; year:2018 ; number:12 ; day:12 ; month:12 ; pages:16011-16032 |
| Links: |
|---|
| DOI / URN: |
10.1007/s11042-018-6956-7 |
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OLC2035064570 |
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| 520 | |a Abstract In the research on computer vision, object tracking has encountered various challenges, such as occlusion and scale variation. In recent years, tracking-by-detection methods have performed competitively. Some of these methods have focused on solving the problem of scale variation. Regardless, these algorithms perform poorly in real time. Recently, correlation filters have been widely used in object tracking because of their high efficiency; however, conventional correlation filter-based trackers cannot handle scale variation. Most correlation filter-based trackers update the template for each frame, and tracking offsets occur when a tracking error is present. To overcome these problems, we propose a novel scale-adaptive tracking algorithm that uses perceptual hash and correlation filter on the basis of tracking-by-detection methods. We employ kernel ridge regression to minimize the mean square error between the training image and the regression object, and construct a robust filter template to track the target center location. By tracking the 4 sub-blocks of the target image, the length and width expansion coefficients are calculated separately to update the target scale. We finally use the adaptive update strategy based on perceptual hash to effectively prevent the tracking offset caused by the template update error. Owing to the insensitivity to the scale variation and high efficiency of the perceptual hash, tracking becomes more robust in real time. Both quantitative and qualitative evaluations on Object Tracking Benchmark (OTB) indicate that the proposed tracking method performs more favorably compared with other state-of-the-art methods. | ||
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| 700 | 1 | |a Huang, Tianqiang |4 aut | |
| 700 | 1 | |a Lin, Jing |4 aut | |
| 700 | 1 | |a Zhang, Xueli |4 aut | |
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10.1007/s11042-018-6956-7 doi (DE-627)OLC2035064570 (DE-He213)s11042-018-6956-7-p DE-627 ger DE-627 rakwb eng 070 004 VZ Huang, Wei verfasserin aut Scale-adaptive tracking based on perceptual hash and correlation filter 2018 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract In the research on computer vision, object tracking has encountered various challenges, such as occlusion and scale variation. In recent years, tracking-by-detection methods have performed competitively. Some of these methods have focused on solving the problem of scale variation. Regardless, these algorithms perform poorly in real time. Recently, correlation filters have been widely used in object tracking because of their high efficiency; however, conventional correlation filter-based trackers cannot handle scale variation. Most correlation filter-based trackers update the template for each frame, and tracking offsets occur when a tracking error is present. To overcome these problems, we propose a novel scale-adaptive tracking algorithm that uses perceptual hash and correlation filter on the basis of tracking-by-detection methods. We employ kernel ridge regression to minimize the mean square error between the training image and the regression object, and construct a robust filter template to track the target center location. By tracking the 4 sub-blocks of the target image, the length and width expansion coefficients are calculated separately to update the target scale. We finally use the adaptive update strategy based on perceptual hash to effectively prevent the tracking offset caused by the template update error. Owing to the insensitivity to the scale variation and high efficiency of the perceptual hash, tracking becomes more robust in real time. Both quantitative and qualitative evaluations on Object Tracking Benchmark (OTB) indicate that the proposed tracking method performs more favorably compared with other state-of-the-art methods. Visual tracking Correlation filter Scale-adaptive Perceptual hash Lin, Lingpeng (orcid)0000-0002-3981-0524 aut Huang, Tianqiang aut Lin, Jing aut Zhang, Xueli aut Enthalten in Multimedia tools and applications Springer US, 1995 78(2018), 12 vom: 12. Dez., Seite 16011-16032 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:78 year:2018 number:12 day:12 month:12 pages:16011-16032 https://doi.org/10.1007/s11042-018-6956-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW GBV_ILN_70 AR 78 2018 12 12 12 16011-16032 |
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10.1007/s11042-018-6956-7 doi (DE-627)OLC2035064570 (DE-He213)s11042-018-6956-7-p DE-627 ger DE-627 rakwb eng 070 004 VZ Huang, Wei verfasserin aut Scale-adaptive tracking based on perceptual hash and correlation filter 2018 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract In the research on computer vision, object tracking has encountered various challenges, such as occlusion and scale variation. In recent years, tracking-by-detection methods have performed competitively. Some of these methods have focused on solving the problem of scale variation. Regardless, these algorithms perform poorly in real time. Recently, correlation filters have been widely used in object tracking because of their high efficiency; however, conventional correlation filter-based trackers cannot handle scale variation. Most correlation filter-based trackers update the template for each frame, and tracking offsets occur when a tracking error is present. To overcome these problems, we propose a novel scale-adaptive tracking algorithm that uses perceptual hash and correlation filter on the basis of tracking-by-detection methods. We employ kernel ridge regression to minimize the mean square error between the training image and the regression object, and construct a robust filter template to track the target center location. By tracking the 4 sub-blocks of the target image, the length and width expansion coefficients are calculated separately to update the target scale. We finally use the adaptive update strategy based on perceptual hash to effectively prevent the tracking offset caused by the template update error. Owing to the insensitivity to the scale variation and high efficiency of the perceptual hash, tracking becomes more robust in real time. Both quantitative and qualitative evaluations on Object Tracking Benchmark (OTB) indicate that the proposed tracking method performs more favorably compared with other state-of-the-art methods. Visual tracking Correlation filter Scale-adaptive Perceptual hash Lin, Lingpeng (orcid)0000-0002-3981-0524 aut Huang, Tianqiang aut Lin, Jing aut Zhang, Xueli aut Enthalten in Multimedia tools and applications Springer US, 1995 78(2018), 12 vom: 12. Dez., Seite 16011-16032 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:78 year:2018 number:12 day:12 month:12 pages:16011-16032 https://doi.org/10.1007/s11042-018-6956-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW GBV_ILN_70 AR 78 2018 12 12 12 16011-16032 |
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10.1007/s11042-018-6956-7 doi (DE-627)OLC2035064570 (DE-He213)s11042-018-6956-7-p DE-627 ger DE-627 rakwb eng 070 004 VZ Huang, Wei verfasserin aut Scale-adaptive tracking based on perceptual hash and correlation filter 2018 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract In the research on computer vision, object tracking has encountered various challenges, such as occlusion and scale variation. In recent years, tracking-by-detection methods have performed competitively. Some of these methods have focused on solving the problem of scale variation. Regardless, these algorithms perform poorly in real time. Recently, correlation filters have been widely used in object tracking because of their high efficiency; however, conventional correlation filter-based trackers cannot handle scale variation. Most correlation filter-based trackers update the template for each frame, and tracking offsets occur when a tracking error is present. To overcome these problems, we propose a novel scale-adaptive tracking algorithm that uses perceptual hash and correlation filter on the basis of tracking-by-detection methods. We employ kernel ridge regression to minimize the mean square error between the training image and the regression object, and construct a robust filter template to track the target center location. By tracking the 4 sub-blocks of the target image, the length and width expansion coefficients are calculated separately to update the target scale. We finally use the adaptive update strategy based on perceptual hash to effectively prevent the tracking offset caused by the template update error. Owing to the insensitivity to the scale variation and high efficiency of the perceptual hash, tracking becomes more robust in real time. Both quantitative and qualitative evaluations on Object Tracking Benchmark (OTB) indicate that the proposed tracking method performs more favorably compared with other state-of-the-art methods. Visual tracking Correlation filter Scale-adaptive Perceptual hash Lin, Lingpeng (orcid)0000-0002-3981-0524 aut Huang, Tianqiang aut Lin, Jing aut Zhang, Xueli aut Enthalten in Multimedia tools and applications Springer US, 1995 78(2018), 12 vom: 12. Dez., Seite 16011-16032 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:78 year:2018 number:12 day:12 month:12 pages:16011-16032 https://doi.org/10.1007/s11042-018-6956-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW GBV_ILN_70 AR 78 2018 12 12 12 16011-16032 |
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Huang, Wei |
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10.1007/s11042-018-6956-7 |
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scale-adaptive tracking based on perceptual hash and correlation filter |
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Scale-adaptive tracking based on perceptual hash and correlation filter |
| abstract |
Abstract In the research on computer vision, object tracking has encountered various challenges, such as occlusion and scale variation. In recent years, tracking-by-detection methods have performed competitively. Some of these methods have focused on solving the problem of scale variation. Regardless, these algorithms perform poorly in real time. Recently, correlation filters have been widely used in object tracking because of their high efficiency; however, conventional correlation filter-based trackers cannot handle scale variation. Most correlation filter-based trackers update the template for each frame, and tracking offsets occur when a tracking error is present. To overcome these problems, we propose a novel scale-adaptive tracking algorithm that uses perceptual hash and correlation filter on the basis of tracking-by-detection methods. We employ kernel ridge regression to minimize the mean square error between the training image and the regression object, and construct a robust filter template to track the target center location. By tracking the 4 sub-blocks of the target image, the length and width expansion coefficients are calculated separately to update the target scale. We finally use the adaptive update strategy based on perceptual hash to effectively prevent the tracking offset caused by the template update error. Owing to the insensitivity to the scale variation and high efficiency of the perceptual hash, tracking becomes more robust in real time. Both quantitative and qualitative evaluations on Object Tracking Benchmark (OTB) indicate that the proposed tracking method performs more favorably compared with other state-of-the-art methods. © Springer Science+Business Media, LLC, part of Springer Nature 2018 |
| abstractGer |
Abstract In the research on computer vision, object tracking has encountered various challenges, such as occlusion and scale variation. In recent years, tracking-by-detection methods have performed competitively. Some of these methods have focused on solving the problem of scale variation. Regardless, these algorithms perform poorly in real time. Recently, correlation filters have been widely used in object tracking because of their high efficiency; however, conventional correlation filter-based trackers cannot handle scale variation. Most correlation filter-based trackers update the template for each frame, and tracking offsets occur when a tracking error is present. To overcome these problems, we propose a novel scale-adaptive tracking algorithm that uses perceptual hash and correlation filter on the basis of tracking-by-detection methods. We employ kernel ridge regression to minimize the mean square error between the training image and the regression object, and construct a robust filter template to track the target center location. By tracking the 4 sub-blocks of the target image, the length and width expansion coefficients are calculated separately to update the target scale. We finally use the adaptive update strategy based on perceptual hash to effectively prevent the tracking offset caused by the template update error. Owing to the insensitivity to the scale variation and high efficiency of the perceptual hash, tracking becomes more robust in real time. Both quantitative and qualitative evaluations on Object Tracking Benchmark (OTB) indicate that the proposed tracking method performs more favorably compared with other state-of-the-art methods. © Springer Science+Business Media, LLC, part of Springer Nature 2018 |
| abstract_unstemmed |
Abstract In the research on computer vision, object tracking has encountered various challenges, such as occlusion and scale variation. In recent years, tracking-by-detection methods have performed competitively. Some of these methods have focused on solving the problem of scale variation. Regardless, these algorithms perform poorly in real time. Recently, correlation filters have been widely used in object tracking because of their high efficiency; however, conventional correlation filter-based trackers cannot handle scale variation. Most correlation filter-based trackers update the template for each frame, and tracking offsets occur when a tracking error is present. To overcome these problems, we propose a novel scale-adaptive tracking algorithm that uses perceptual hash and correlation filter on the basis of tracking-by-detection methods. We employ kernel ridge regression to minimize the mean square error between the training image and the regression object, and construct a robust filter template to track the target center location. By tracking the 4 sub-blocks of the target image, the length and width expansion coefficients are calculated separately to update the target scale. We finally use the adaptive update strategy based on perceptual hash to effectively prevent the tracking offset caused by the template update error. Owing to the insensitivity to the scale variation and high efficiency of the perceptual hash, tracking becomes more robust in real time. Both quantitative and qualitative evaluations on Object Tracking Benchmark (OTB) indicate that the proposed tracking method performs more favorably compared with other state-of-the-art methods. © Springer Science+Business Media, LLC, part of Springer Nature 2018 |
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Scale-adaptive tracking based on perceptual hash and correlation filter |
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