Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/107146
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Electrical and Electronic Engineering | - |
dc.creator | Chen, YQ | - |
dc.creator | Sun, ZL | - |
dc.creator | Lam, KM | - |
dc.date.accessioned | 2024-06-13T01:04:11Z | - |
dc.date.available | 2024-06-13T01:04:11Z | - |
dc.identifier.issn | 0278-0046 | - |
dc.identifier.uri | http://hdl.handle.net/10397/107146 | - |
dc.language.iso | en | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers | en_US |
dc.rights | © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | en_US |
dc.rights | The following publication Y. -Q. Chen, Z. -L. Sun and K. -M. Lam, "An Effective Subsuperpixel-Based Approach for Background Subtraction," in IEEE Transactions on Industrial Electronics, vol. 67, no. 1, pp. 601-609, Jan. 2020 is available at https://doi.org/10.1109/TIE.2019.2893824. | en_US |
dc.subject | Background subtraction | en_US |
dc.subject | K-means clustering | en_US |
dc.subject | Superpixel | en_US |
dc.title | An effective subsuperpixel-based approach for background subtraction | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.spage | 601 | - |
dc.identifier.epage | 609 | - |
dc.identifier.volume | 67 | - |
dc.identifier.issue | 1 | - |
dc.identifier.doi | 10.1109/TIE.2019.2893824 | - |
dcterms.abstract | How to achieve competitive accuracy and less computation time simultaneously for background estimation is still an intractable task. In this paper, an effective background subtraction approach for video sequences is proposed based on a subsuperpixel model. In our algorithm, the superpixels of the first frame are constructed using a simple linear iterative clustering method. After transforming the frame from a color format to gray level, the initial superpixels are divided into K smaller units, i.e., subsuperpixels, via the k-means clustering algorithm. The background model is then initialized by representing each subsuperpixel as a multidimensional feature vector. For the subsequent frames, moving objects are detected by the subsuperpixel representation and a weighting measure. In order to deal with ghost artifacts, a background model updating strategy is devised, based on the number of pixels represented by each cluster center. As each superpixel is refined via the subsuperpixel representation, the proposed method is more efficient and achieves a competitive accuracy for background subtraction. Experimental results demonstrate the effectiveness of the proposed method. | - |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | IEEE transactions on industrial electronics, Jan. 2020, v. 67, no. 1, p. 601-609 | - |
dcterms.isPartOf | IEEE transactions on industrial electronics | - |
dcterms.issued | 2020-01 | - |
dc.identifier.scopus | 2-s2.0-85072127966 | - |
dc.identifier.eissn | 1557-9948 | - |
dc.description.validate | 202403 bckw | - |
dc.description.oa | Accepted Manuscript | en_US |
dc.identifier.FolderNumber | EIE-0243 | en_US |
dc.description.fundingSource | Others | en_US |
dc.description.fundingText | National Natural Science Foundation of China; Key Project of Support Program for Outstanding Young Talents of Anhui Province University; Science and Technology Program to strengthen police force; Academic and Technical Leaders and Candidates of Anhui Province; Anhui Province Key Laboratory of Nondestructive Evaluation | en_US |
dc.description.pubStatus | Published | en_US |
dc.identifier.OPUS | 20083762 | en_US |
dc.description.oaCategory | Green (AAM) | en_US |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Lam_Effective_Subsuperpixel-Based_Approach.pdf | Pre-Published version | 3.3 MB | Adobe PDF | View/Open |
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