Video Saliency Map Detection by Dominant Camera Motion Removal.
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| Title: | Video Saliency Map Detection by Dominant Camera Motion Removal. |
|---|---|
| Authors: | Huang, Chun-Rong1, Chang, Yun-Jung1, Yang, Zhi-Xiang1, Lin, Yen-Yu2 |
| Source: | IEEE Transactions on Circuits & Systems for Video Technology. Aug2014, Vol. 24 Issue 8, p1336-1349. 14p. |
| Subjects: | Visualization, Feature extraction, Support vector machines, Trajectory optimization, Cameras, Video recording |
| Abstract: | We present a trajectory-based approach to detect salient regions in videos by dominant camera motion removal. Our approach is designed in a general way so that it can be applied to videos taken by either stationary or moving cameras without any prior information. Moreover, multiple salient regions of different temporal lengths can also be detected. To this end, we extract a set of spatially and temporally coherent trajectories of keypoints in a video. Then, velocity and acceleration entropies are proposed to represent the trajectories. In this way, long-term object motions are exploited to filter out short-term noises, and object motions of various temporal lengths can be represented in the same way. On the other hand, we are inspired by the observation that the trajectories in backgrounds, i.e., the nonsalient trajectories, are usually consistent with the dominant camera motion no matter whether the camera is stationary or not. We make use of this property to develop a unified approach to saliency generation for both stationary and moving cameras. Specifically, one-class SVM is employed to remove the consistent trajectories in motion. It follows that the salient regions could be highlighted by applying a diffusion process to the remaining trajectories. In addition, we create a set of manually annotated ground truth on the collected videos. The annotated videos are then used for performance evaluation and comparison. The promising results on various types of videos demonstrate the effectiveness and great applicability of our approach. [ABSTRACT FROM AUTHOR] |
| Copyright of IEEE Transactions on Circuits & Systems for Video Technology is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 97379879 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Video Saliency Map Detection by Dominant Camera Motion Removal. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Huang%2C+Chun-Rong%22">Huang, Chun-Rong</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Chang%2C+Yun-Jung%22">Chang, Yun-Jung</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Yang%2C+Zhi-Xiang%22">Yang, Zhi-Xiang</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Lin%2C+Yen-Yu%22">Lin, Yen-Yu</searchLink><relatesTo>2</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22IEEE+Transactions+on+Circuits+%26+Systems+for+Video+Technology%22">IEEE Transactions on Circuits & Systems for Video Technology</searchLink>. Aug2014, Vol. 24 Issue 8, p1336-1349. 14p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Visualization%22">Visualization</searchLink><br /><searchLink fieldCode="DE" term="%22Feature+extraction%22">Feature extraction</searchLink><br /><searchLink fieldCode="DE" term="%22Support+vector+machines%22">Support vector machines</searchLink><br /><searchLink fieldCode="DE" term="%22Trajectory+optimization%22">Trajectory optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Cameras%22">Cameras</searchLink><br /><searchLink fieldCode="DE" term="%22Video+recording%22">Video recording</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: We present a trajectory-based approach to detect salient regions in videos by dominant camera motion removal. Our approach is designed in a general way so that it can be applied to videos taken by either stationary or moving cameras without any prior information. Moreover, multiple salient regions of different temporal lengths can also be detected. To this end, we extract a set of spatially and temporally coherent trajectories of keypoints in a video. Then, velocity and acceleration entropies are proposed to represent the trajectories. In this way, long-term object motions are exploited to filter out short-term noises, and object motions of various temporal lengths can be represented in the same way. On the other hand, we are inspired by the observation that the trajectories in backgrounds, i.e., the nonsalient trajectories, are usually consistent with the dominant camera motion no matter whether the camera is stationary or not. We make use of this property to develop a unified approach to saliency generation for both stationary and moving cameras. Specifically, one-class SVM is employed to remove the consistent trajectories in motion. It follows that the salient regions could be highlighted by applying a diffusion process to the remaining trajectories. In addition, we create a set of manually annotated ground truth on the collected videos. The annotated videos are then used for performance evaluation and comparison. The promising results on various types of videos demonstrate the effectiveness and great applicability of our approach. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of IEEE Transactions on Circuits & Systems for Video Technology is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1109/TCSVT.2014.2308652 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 14 StartPage: 1336 Subjects: – SubjectFull: Visualization Type: general – SubjectFull: Feature extraction Type: general – SubjectFull: Support vector machines Type: general – SubjectFull: Trajectory optimization Type: general – SubjectFull: Cameras Type: general – SubjectFull: Video recording Type: general Titles: – TitleFull: Video Saliency Map Detection by Dominant Camera Motion Removal. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Huang, Chun-Rong – PersonEntity: Name: NameFull: Chang, Yun-Jung – PersonEntity: Name: NameFull: Yang, Zhi-Xiang – PersonEntity: Name: NameFull: Lin, Yen-Yu IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 08 Text: Aug2014 Type: published Y: 2014 Identifiers: – Type: issn-print Value: 10518215 Numbering: – Type: volume Value: 24 – Type: issue Value: 8 Titles: – TitleFull: IEEE Transactions on Circuits & Systems for Video Technology Type: main |
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