Loading…
Dim moving target detection algorithm based on spatio-temporal classification sparse representation
•Spatio-temporal dictionary can characterize motion and morphology.•Target can be sparsely decomposed on target spatio-temporal dictionary.•Background can be sparsely decomposed on background spatio-temporal dictionary.•Target can be decomposed more sparsely on Gaussian spatio-temporal dictionary.•T...
Saved in:
Published in: | Infrared physics & technology 2014-11, Vol.67, p.273-282 |
---|---|
Main Authors: | , , , , , , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
cited_by | cdi_FETCH-LOGICAL-c345t-8f98e9e5a6899e51981d84a3b776fafa5b06776383a6f15068bc258f183fbdc43 |
---|---|
cites | cdi_FETCH-LOGICAL-c345t-8f98e9e5a6899e51981d84a3b776fafa5b06776383a6f15068bc258f183fbdc43 |
container_end_page | 282 |
container_issue | |
container_start_page | 273 |
container_title | Infrared physics & technology |
container_volume | 67 |
creator | Li, Zhengzhou Dai, Zhen Fu, Hongxia Hou, Qian Wang, Zhen Yang, Lijiao Jin, Gang Liu, Changju Li, Ruzhang |
description | •Spatio-temporal dictionary can characterize motion and morphology.•Target can be sparsely decomposed on target spatio-temporal dictionary.•Background can be sparsely decomposed on background spatio-temporal dictionary.•Target can be decomposed more sparsely on Gaussian spatio-temporal dictionary.•The residuals reconstructed by target and background atoms differ very visibly.
A dim moving target detection algorithm based on spatio-temporal classification sparse representation, which can characterize the motion information and morphological feature of target and background clutter, is proposed to enhance the performance of target detection. A spatio-temporal redundant dictionary is trained according to the content of infrared image sequence, and then is subdivided into target spatio-temporal redundant dictionary describing moving target, and background spatio-temporal redundant dictionary embedding background by the criterion that the target spatio-temporal atom could be decomposed more sparsely over Gaussian spatio-temporal redundant dictionary. The target and background clutter can be sparsely decomposed over their corresponding spatio-temporal redundant dictionary, yet could not be sparsely decomposed on their opposite spatio-temporal redundant dictionary, and so their residuals after reconstruction by the prescribed number of target and background spatio-temporal atoms would differ very visibly. Some experimental results show this proposed approach could not only improve the sparsity more efficiently, but also enhance the target detection performance more effectively. |
doi_str_mv | 10.1016/j.infrared.2014.07.030 |
format | article |
fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_1651457630</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><els_id>S1350449514001546</els_id><sourcerecordid>1651457630</sourcerecordid><originalsourceid>FETCH-LOGICAL-c345t-8f98e9e5a6899e51981d84a3b776fafa5b06776383a6f15068bc258f183fbdc43</originalsourceid><addsrcrecordid>eNqFUMtOwzAQtBBIlMIvIB-5JNh17Dg3UHlKlbjA2XKcdXGVxMF2K_H3uC2cOc1o5yHtIHRNSUkJFbeb0o026ABduSC0KkldEkZO0IzKuinIouanmTNOiqpq-Dm6iHFDcrAiYobMgxvw4HduXOOkwxoS7iCBSc6PWPdrH1z6HHCrI3Q4n-Kks1QkGCYfdI9Nr2N01hl9SGQ5RMABpgARxnS4XqIzq_sIV784Rx9Pj-_Ll2L19vy6vF8VhlU8FdI2EhrgWsgmA20k7WSlWVvXwmqreUtEpkwyLSzlRMjWLLi0VDLbdqZic3Rz7J2C_9pCTGpw0UDf6xH8NioqOK14biDZKo5WE3yMAayaght0-FaUqP2qaqP-VlX7VRWpFTkE745ByI_sHAQVjYPRQOdCXk113v1X8QN4iIY8</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1651457630</pqid></control><display><type>article</type><title>Dim moving target detection algorithm based on spatio-temporal classification sparse representation</title><source>Elsevier</source><creator>Li, Zhengzhou ; Dai, Zhen ; Fu, Hongxia ; Hou, Qian ; Wang, Zhen ; Yang, Lijiao ; Jin, Gang ; Liu, Changju ; Li, Ruzhang</creator><creatorcontrib>Li, Zhengzhou ; Dai, Zhen ; Fu, Hongxia ; Hou, Qian ; Wang, Zhen ; Yang, Lijiao ; Jin, Gang ; Liu, Changju ; Li, Ruzhang</creatorcontrib><description>•Spatio-temporal dictionary can characterize motion and morphology.•Target can be sparsely decomposed on target spatio-temporal dictionary.•Background can be sparsely decomposed on background spatio-temporal dictionary.•Target can be decomposed more sparsely on Gaussian spatio-temporal dictionary.•The residuals reconstructed by target and background atoms differ very visibly.
A dim moving target detection algorithm based on spatio-temporal classification sparse representation, which can characterize the motion information and morphological feature of target and background clutter, is proposed to enhance the performance of target detection. A spatio-temporal redundant dictionary is trained according to the content of infrared image sequence, and then is subdivided into target spatio-temporal redundant dictionary describing moving target, and background spatio-temporal redundant dictionary embedding background by the criterion that the target spatio-temporal atom could be decomposed more sparsely over Gaussian spatio-temporal redundant dictionary. The target and background clutter can be sparsely decomposed over their corresponding spatio-temporal redundant dictionary, yet could not be sparsely decomposed on their opposite spatio-temporal redundant dictionary, and so their residuals after reconstruction by the prescribed number of target and background spatio-temporal atoms would differ very visibly. Some experimental results show this proposed approach could not only improve the sparsity more efficiently, but also enhance the target detection performance more effectively.</description><identifier>ISSN: 1350-4495</identifier><identifier>EISSN: 1879-0275</identifier><identifier>DOI: 10.1016/j.infrared.2014.07.030</identifier><language>eng</language><publisher>Elsevier B.V</publisher><subject>Algorithms ; Background spatio-temporal redundant dictionary ; Classification ; Clutter ; Decomposition ; Dictionaries ; Dim target detection ; Moving targets ; Redundant ; Representations ; Signal sparse reconstruction ; Spatio-temporal classification redundant dictionary ; Target spatio-temporal redundant dictionary</subject><ispartof>Infrared physics & technology, 2014-11, Vol.67, p.273-282</ispartof><rights>2014 Elsevier B.V.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c345t-8f98e9e5a6899e51981d84a3b776fafa5b06776383a6f15068bc258f183fbdc43</citedby><cites>FETCH-LOGICAL-c345t-8f98e9e5a6899e51981d84a3b776fafa5b06776383a6f15068bc258f183fbdc43</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids></links><search><creatorcontrib>Li, Zhengzhou</creatorcontrib><creatorcontrib>Dai, Zhen</creatorcontrib><creatorcontrib>Fu, Hongxia</creatorcontrib><creatorcontrib>Hou, Qian</creatorcontrib><creatorcontrib>Wang, Zhen</creatorcontrib><creatorcontrib>Yang, Lijiao</creatorcontrib><creatorcontrib>Jin, Gang</creatorcontrib><creatorcontrib>Liu, Changju</creatorcontrib><creatorcontrib>Li, Ruzhang</creatorcontrib><title>Dim moving target detection algorithm based on spatio-temporal classification sparse representation</title><title>Infrared physics & technology</title><description>•Spatio-temporal dictionary can characterize motion and morphology.•Target can be sparsely decomposed on target spatio-temporal dictionary.•Background can be sparsely decomposed on background spatio-temporal dictionary.•Target can be decomposed more sparsely on Gaussian spatio-temporal dictionary.•The residuals reconstructed by target and background atoms differ very visibly.
A dim moving target detection algorithm based on spatio-temporal classification sparse representation, which can characterize the motion information and morphological feature of target and background clutter, is proposed to enhance the performance of target detection. A spatio-temporal redundant dictionary is trained according to the content of infrared image sequence, and then is subdivided into target spatio-temporal redundant dictionary describing moving target, and background spatio-temporal redundant dictionary embedding background by the criterion that the target spatio-temporal atom could be decomposed more sparsely over Gaussian spatio-temporal redundant dictionary. The target and background clutter can be sparsely decomposed over their corresponding spatio-temporal redundant dictionary, yet could not be sparsely decomposed on their opposite spatio-temporal redundant dictionary, and so their residuals after reconstruction by the prescribed number of target and background spatio-temporal atoms would differ very visibly. Some experimental results show this proposed approach could not only improve the sparsity more efficiently, but also enhance the target detection performance more effectively.</description><subject>Algorithms</subject><subject>Background spatio-temporal redundant dictionary</subject><subject>Classification</subject><subject>Clutter</subject><subject>Decomposition</subject><subject>Dictionaries</subject><subject>Dim target detection</subject><subject>Moving targets</subject><subject>Redundant</subject><subject>Representations</subject><subject>Signal sparse reconstruction</subject><subject>Spatio-temporal classification redundant dictionary</subject><subject>Target spatio-temporal redundant dictionary</subject><issn>1350-4495</issn><issn>1879-0275</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2014</creationdate><recordtype>article</recordtype><recordid>eNqFUMtOwzAQtBBIlMIvIB-5JNh17Dg3UHlKlbjA2XKcdXGVxMF2K_H3uC2cOc1o5yHtIHRNSUkJFbeb0o026ABduSC0KkldEkZO0IzKuinIouanmTNOiqpq-Dm6iHFDcrAiYobMgxvw4HduXOOkwxoS7iCBSc6PWPdrH1z6HHCrI3Q4n-Kks1QkGCYfdI9Nr2N01hl9SGQ5RMABpgARxnS4XqIzq_sIV784Rx9Pj-_Ll2L19vy6vF8VhlU8FdI2EhrgWsgmA20k7WSlWVvXwmqreUtEpkwyLSzlRMjWLLi0VDLbdqZic3Rz7J2C_9pCTGpw0UDf6xH8NioqOK14biDZKo5WE3yMAayaght0-FaUqP2qaqP-VlX7VRWpFTkE745ByI_sHAQVjYPRQOdCXk113v1X8QN4iIY8</recordid><startdate>20141101</startdate><enddate>20141101</enddate><creator>Li, Zhengzhou</creator><creator>Dai, Zhen</creator><creator>Fu, Hongxia</creator><creator>Hou, Qian</creator><creator>Wang, Zhen</creator><creator>Yang, Lijiao</creator><creator>Jin, Gang</creator><creator>Liu, Changju</creator><creator>Li, Ruzhang</creator><general>Elsevier B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SP</scope><scope>7U5</scope><scope>8FD</scope><scope>H8D</scope><scope>L7M</scope></search><sort><creationdate>20141101</creationdate><title>Dim moving target detection algorithm based on spatio-temporal classification sparse representation</title><author>Li, Zhengzhou ; Dai, Zhen ; Fu, Hongxia ; Hou, Qian ; Wang, Zhen ; Yang, Lijiao ; Jin, Gang ; Liu, Changju ; Li, Ruzhang</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c345t-8f98e9e5a6899e51981d84a3b776fafa5b06776383a6f15068bc258f183fbdc43</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2014</creationdate><topic>Algorithms</topic><topic>Background spatio-temporal redundant dictionary</topic><topic>Classification</topic><topic>Clutter</topic><topic>Decomposition</topic><topic>Dictionaries</topic><topic>Dim target detection</topic><topic>Moving targets</topic><topic>Redundant</topic><topic>Representations</topic><topic>Signal sparse reconstruction</topic><topic>Spatio-temporal classification redundant dictionary</topic><topic>Target spatio-temporal redundant dictionary</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Li, Zhengzhou</creatorcontrib><creatorcontrib>Dai, Zhen</creatorcontrib><creatorcontrib>Fu, Hongxia</creatorcontrib><creatorcontrib>Hou, Qian</creatorcontrib><creatorcontrib>Wang, Zhen</creatorcontrib><creatorcontrib>Yang, Lijiao</creatorcontrib><creatorcontrib>Jin, Gang</creatorcontrib><creatorcontrib>Liu, Changju</creatorcontrib><creatorcontrib>Li, Ruzhang</creatorcontrib><collection>CrossRef</collection><collection>Electronics & Communications Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>Technology Research Database</collection><collection>Aerospace Database</collection><collection>Advanced Technologies Database with Aerospace</collection><jtitle>Infrared physics & technology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Li, Zhengzhou</au><au>Dai, Zhen</au><au>Fu, Hongxia</au><au>Hou, Qian</au><au>Wang, Zhen</au><au>Yang, Lijiao</au><au>Jin, Gang</au><au>Liu, Changju</au><au>Li, Ruzhang</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Dim moving target detection algorithm based on spatio-temporal classification sparse representation</atitle><jtitle>Infrared physics & technology</jtitle><date>2014-11-01</date><risdate>2014</risdate><volume>67</volume><spage>273</spage><epage>282</epage><pages>273-282</pages><issn>1350-4495</issn><eissn>1879-0275</eissn><abstract>•Spatio-temporal dictionary can characterize motion and morphology.•Target can be sparsely decomposed on target spatio-temporal dictionary.•Background can be sparsely decomposed on background spatio-temporal dictionary.•Target can be decomposed more sparsely on Gaussian spatio-temporal dictionary.•The residuals reconstructed by target and background atoms differ very visibly.
A dim moving target detection algorithm based on spatio-temporal classification sparse representation, which can characterize the motion information and morphological feature of target and background clutter, is proposed to enhance the performance of target detection. A spatio-temporal redundant dictionary is trained according to the content of infrared image sequence, and then is subdivided into target spatio-temporal redundant dictionary describing moving target, and background spatio-temporal redundant dictionary embedding background by the criterion that the target spatio-temporal atom could be decomposed more sparsely over Gaussian spatio-temporal redundant dictionary. The target and background clutter can be sparsely decomposed over their corresponding spatio-temporal redundant dictionary, yet could not be sparsely decomposed on their opposite spatio-temporal redundant dictionary, and so their residuals after reconstruction by the prescribed number of target and background spatio-temporal atoms would differ very visibly. Some experimental results show this proposed approach could not only improve the sparsity more efficiently, but also enhance the target detection performance more effectively.</abstract><pub>Elsevier B.V</pub><doi>10.1016/j.infrared.2014.07.030</doi><tpages>10</tpages></addata></record> |
fulltext | fulltext |
identifier | ISSN: 1350-4495 |
ispartof | Infrared physics & technology, 2014-11, Vol.67, p.273-282 |
issn | 1350-4495 1879-0275 |
language | eng |
recordid | cdi_proquest_miscellaneous_1651457630 |
source | Elsevier |
subjects | Algorithms Background spatio-temporal redundant dictionary Classification Clutter Decomposition Dictionaries Dim target detection Moving targets Redundant Representations Signal sparse reconstruction Spatio-temporal classification redundant dictionary Target spatio-temporal redundant dictionary |
title | Dim moving target detection algorithm based on spatio-temporal classification sparse representation |
url | http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-02T09%3A30%3A07IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Dim%20moving%20target%20detection%20algorithm%20based%20on%20spatio-temporal%20classification%20sparse%20representation&rft.jtitle=Infrared%20physics%20&%20technology&rft.au=Li,%20Zhengzhou&rft.date=2014-11-01&rft.volume=67&rft.spage=273&rft.epage=282&rft.pages=273-282&rft.issn=1350-4495&rft.eissn=1879-0275&rft_id=info:doi/10.1016/j.infrared.2014.07.030&rft_dat=%3Cproquest_cross%3E1651457630%3C/proquest_cross%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c345t-8f98e9e5a6899e51981d84a3b776fafa5b06776383a6f15068bc258f183fbdc43%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=1651457630&rft_id=info:pmid/&rfr_iscdi=true |