Detection of Moving Object in Dynamic Visual Sequences Based on Partial Least Squares Classifier.
Detection of moving object from a visual sequence plays a vital role for the tracking of object. The main objective of this proposed work is to detect and classify the various video sequences with the help of different classification algorithms. The input video sequences from the publicly available...
| Publicado en: | Journal of Medical Systems Vol. 43; no. 8 |
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| Autores principales: | , , , |
| Formato: | equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Aug2019
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=137490045&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 137490045 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: Aug2019 vid: 43 iid: 8 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 137490045 137490045 137490045 10.1007/s10916-019-1386-2 137490045 ppct: 1 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Detection of Moving Object in Dynamic Visual Sequences Based on Partial Least Squares Classifier. aug: au: Balakumar, Shyamala Sundaramoorthy, Selvaperumal Bhoopalan, Ramasubramanian Prabhakar, G. affil: Department of ECE, Mohamed Sathak Engineering College, Kilakarai, Ramanathapuram, India sug: subj: Videorecording Motion Analysis Systems Methods Motion Analysis Systems Classification Algorithms Evaluation Image Enhancement Methods Informatics Noise Subtraction Technique Descriptive Statistics Regression Methods Probability Neural Networks (Computer) Comparative Studies ab: Detection of moving object from a visual sequence plays a vital role for the tracking of object. The main objective of this proposed work is to detect and classify the various video sequences with the help of different classification algorithms. The input video sequences from the publicly available datasets are collected and the individual frames are extracted. These frames are pre-processed and then applied to the novel background subtraction process. Important features based on the Local Binary Pattern (LBP) and grey level co-efficient are extracted. Finally these features are classified by three different classifiers like SVM, PLS, and PNN. The performance of these different classifiers are evaluated and compared. It is found that PLS classifier produces more classification accuracy but with more computation time. pubtype: Academic Journal doctype: equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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