Robust Privacy-Preserving Motion Detection and Object Tracking in Encrypted Streaming Video
Video privacy leakage is becoming an increasingly severe public problem, especially in cloud-based video surveillance systems. It leads to the new need for secure cloud-based video applications, where the video is encrypted for privacy protection. Despite some methods that have been proposed for enc...
Gespeichert in:
| Veröffentlicht in: | IEEE transactions on information forensics and security Jg. 16; S. 5381 - 5396 |
|---|---|
| Hauptverfasser: | , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
New York
IEEE
2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Schlagworte: | |
| ISSN: | 1556-6013, 1556-6021 |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| Abstract | Video privacy leakage is becoming an increasingly severe public problem, especially in cloud-based video surveillance systems. It leads to the new need for secure cloud-based video applications, where the video is encrypted for privacy protection. Despite some methods that have been proposed for encrypted video moving object detection and tracking, none has robust performance against complex and dynamic scenes. In this paper, we propose an efficient and robust privacy-preserving motion detection and multiple object tracking scheme for encrypted surveillance video bitstreams. By analyzing the properties of the video codec and format-compliant encryption schemes, we propose a new compressed-domain feature to capture motion information in complex surveillance scenarios. Based on this feature, we design an adaptive clustering algorithm for moving object segmentation with an accuracy of <inline-formula> <tex-math notation="LaTeX">4\times 4 </tex-math></inline-formula> pixels. We then propose a multiple object tracking scheme that uses Kalman filter estimation and adaptive measurement refinement. The proposed scheme does not require video decryption or full decompression and has a very low computation load. The experimental results demonstrate that our scheme achieves the best detection and tracking performance compared with existing works in the encrypted and compressed domain. Our scheme can be effectively used in complex surveillance scenarios with different challenges, such as camera movement/jitter, dynamic background, and shadows. |
|---|---|
| AbstractList | Video privacy leakage is becoming an increasingly severe public problem, especially in cloud-based video surveillance systems. It leads to the new need for secure cloud-based video applications, where the video is encrypted for privacy protection. Despite some methods that have been proposed for encrypted video moving object detection and tracking, none has robust performance against complex and dynamic scenes. In this paper, we propose an efficient and robust privacy-preserving motion detection and multiple object tracking scheme for encrypted surveillance video bitstreams. By analyzing the properties of the video codec and format-compliant encryption schemes, we propose a new compressed-domain feature to capture motion information in complex surveillance scenarios. Based on this feature, we design an adaptive clustering algorithm for moving object segmentation with an accuracy of [Formula Omitted] pixels. We then propose a multiple object tracking scheme that uses Kalman filter estimation and adaptive measurement refinement. The proposed scheme does not require video decryption or full decompression and has a very low computation load. The experimental results demonstrate that our scheme achieves the best detection and tracking performance compared with existing works in the encrypted and compressed domain. Our scheme can be effectively used in complex surveillance scenarios with different challenges, such as camera movement/jitter, dynamic background, and shadows. Video privacy leakage is becoming an increasingly severe public problem, especially in cloud-based video surveillance systems. It leads to the new need for secure cloud-based video applications, where the video is encrypted for privacy protection. Despite some methods that have been proposed for encrypted video moving object detection and tracking, none has robust performance against complex and dynamic scenes. In this paper, we propose an efficient and robust privacy-preserving motion detection and multiple object tracking scheme for encrypted surveillance video bitstreams. By analyzing the properties of the video codec and format-compliant encryption schemes, we propose a new compressed-domain feature to capture motion information in complex surveillance scenarios. Based on this feature, we design an adaptive clustering algorithm for moving object segmentation with an accuracy of <inline-formula> <tex-math notation="LaTeX">4\times 4 </tex-math></inline-formula> pixels. We then propose a multiple object tracking scheme that uses Kalman filter estimation and adaptive measurement refinement. The proposed scheme does not require video decryption or full decompression and has a very low computation load. The experimental results demonstrate that our scheme achieves the best detection and tracking performance compared with existing works in the encrypted and compressed domain. Our scheme can be effectively used in complex surveillance scenarios with different challenges, such as camera movement/jitter, dynamic background, and shadows. |
| Author | Huang, Jiwu Zheng, Peijia Tian, Xianhao |
| Author_xml | – sequence: 1 givenname: Xianhao orcidid: 0000-0002-2485-8365 surname: Tian fullname: Tian, Xianhao organization: Guangdong Key Laboratory of Intelligent Information Processing and Shenzhen Key Laboratory of Media Security, Shenzhen University, Shenzhen, China – sequence: 2 givenname: Peijia orcidid: 0000-0003-0979-8613 surname: Zheng fullname: Zheng, Peijia email: zhpj@mail.sysu.edu.cn organization: School of Computer Science and Engineering and the Guangdong Province Key Laboratory of Information Security Technology, Sun Yat-sen University, Guangzhou, China – sequence: 3 givenname: Jiwu orcidid: 0000-0002-7625-5689 surname: Huang fullname: Huang, Jiwu email: jwhuang@szu.edu.cn organization: Guangdong Key Laboratory of Intelligent Information Processing and Shenzhen Key Laboratory of Media Security, Shenzhen University, Shenzhen, China |
| BookMark | eNp9kF9LwzAUxYMouE0_gPhS8LkzaZqkeZS56WCy4aYvPpQ0uZXMLZ1pNti3t3VjDz4IF-4fzrkHfl107ioHCN0Q3CcEy_vFeDTvJzghfUqSLCPiDHUIYzzmze38NBN6ibp1vcQ4TQnPOujjtSq2dYhm3u6U3sczDzX4nXWf0UsVbOWiRwigfyflTDQtls0WLbzSX63IumjotN9vAphoHjyodXt-twaqK3RRqlUN18feQ2-j4WLwHE-mT-PBwyTWiaQh1tqILDEcjCyIwakUkDHDhEzSjCeUlYyaVFMFMgMsecGA8ZQaIUoigZeC9tDd4e_GV99bqEO-rLbeNZF5wrHIhGCYNypyUGlf1bWHMt94u1Z-nxOctwzzlmHeMsyPDBuP-OPRNqgWRvDKrv513h6cFgBOSZIT0RT9AaJQgNc |
| CODEN | ITIFA6 |
| CitedBy_id | crossref_primary_10_1016_j_engappai_2023_106988 crossref_primary_10_1109_TMC_2024_3504721 crossref_primary_10_1145_3744248 crossref_primary_10_1109_TDSC_2023_3318975 crossref_primary_10_1109_MNET_2024_3368457 crossref_primary_10_3390_app142210254 crossref_primary_10_1109_TIFS_2023_3245984 crossref_primary_10_1109_TITS_2022_3213548 crossref_primary_10_1109_JIOT_2022_3212464 crossref_primary_10_1109_TII_2024_3495789 crossref_primary_10_3390_s25103113 crossref_primary_10_1016_j_neucom_2024_128714 crossref_primary_10_1109_JIOT_2023_3336394 crossref_primary_10_1145_3625825 crossref_primary_10_1145_3698400 crossref_primary_10_1016_j_knosys_2023_110872 crossref_primary_10_1109_TCE_2022_3228679 crossref_primary_10_1016_j_asej_2024_103245 |
| Cites_doi | 10.1007/s11042-016-3578-9 10.1109/TIP.2012.2204272 10.1016/j.cviu.2016.08.005 10.1109/TCSVT.2016.2645616 10.1145/2633600 10.1109/TIP.2016.2568460 10.1109/TCSVT.2013.2255416 10.1109/TCSVT.2017.2742023 10.1145/2886777 10.1109/ICIP.2014.7025068 10.1109/INFOCOM.2017.8056953 10.1109/TIFS.2014.2302899 10.1109/TDSC.2019.2913422 10.1109/CVPRW.2012.6238919 10.1109/TPAMI.2016.2577031 10.1145/2502081.2502105 10.1007/978-3-642-03168-7_14 10.1109/TCSVT.2015.2433194 10.1109/MSP.2012.2219653 10.1109/TIFS.2013.2262273 10.1109/ICINFA.2010.5512258 10.1109/TMM.2017.2777470 10.1109/CVPR.2016.90 10.1145/2502081.2502157 10.1109/TIP.2014.2378053 10.1109/TPDS.2017.2712148 10.1016/j.jvcir.2009.05.001 10.1109/TMM.2012.2187777 10.1016/j.jvcir.2006.03.004 10.1109/TIFS.2016.2590944 10.1109/CVPR.2011.5995586 10.1109/TMM.2013.2281029 10.1109/CVPR.2013.312 10.1007/3-540-48910-X_16 10.1109/CVPRW.2012.6238922 10.1109/TIP.2012.2214049 10.1109/TCSVT.2019.2929855 10.1109/CVPR.2016.91 10.1109/TCSVT.2011.2129090 10.1109/TCSVT.2019.2924910 10.1007/978-3-319-27671-7_47 10.1109/ICCV.2009.5459370 10.1109/34.120329 10.1145/3131342 10.3390/e20010060 10.1109/CVPR.1999.784637 10.1007/11767831_15 10.1109/FG47880.2020.00021 10.1007/s13369-013-0887-4 10.1109/ICIP.2018.8451279 |
| ContentType | Journal Article |
| Copyright | Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2021 |
| Copyright_xml | – notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2021 |
| DBID | 97E RIA RIE AAYXX CITATION 7SC 7SP 7TB 8FD FR3 JQ2 KR7 L7M L~C L~D |
| DOI | 10.1109/TIFS.2021.3128817 |
| DatabaseName | IEEE All-Society Periodicals Package (ASPP) 2005–Present IEEE All-Society Periodicals Package (ASPP) Online IEEE Electronic Library (IEL) CrossRef Computer and Information Systems Abstracts Electronics & Communications Abstracts Mechanical & Transportation Engineering Abstracts Technology Research Database Engineering Research Database ProQuest Computer Science Collection Civil Engineering Abstracts Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional |
| DatabaseTitle | CrossRef Civil Engineering Abstracts Technology Research Database Computer and Information Systems Abstracts – Academic Mechanical & Transportation Engineering Abstracts Electronics & Communications Abstracts ProQuest Computer Science Collection Computer and Information Systems Abstracts Engineering Research Database Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Professional |
| DatabaseTitleList | Civil Engineering Abstracts |
| Database_xml | – sequence: 1 dbid: RIE name: IEEE Electronic Library (IEL) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering Computer Science |
| EISSN | 1556-6021 |
| EndPage | 5396 |
| ExternalDocumentID | 10_1109_TIFS_2021_3128817 9617617 |
| Genre | orig-research |
| GrantInformation_xml | – fundername: Science and Technology Projects in Guangzhou grantid: 202102080354 – fundername: Key-Area Research and Development Program of Guangdong Province grantid: 2019B010139003 – fundername: Shenzhen Research and Development Program grantid: JCYJ20200109105008228; 20200813110043002 funderid: 10.13039/501100017622 – fundername: National Natural Science Foundation of China (NSFC) grantid: U19B2022; 61802262 funderid: 10.13039/501100001809 – fundername: Natural Science Foundation of Guangdong grantid: 2019A1515010746 funderid: 10.13039/501100003453 |
| GroupedDBID | 0R~ 29I 4.4 5GY 5VS 6IK 97E AAJGR AARMG AASAJ AAWTH ABAZT ABQJQ ABVLG ACGFS ACIWK AENEX AETIX AGQYO AGSQL AHBIQ AKJIK AKQYR ALMA_UNASSIGNED_HOLDINGS ATWAV BEFXN BFFAM BGNUA BKEBE BPEOZ CS3 DU5 EBS EJD HZ~ IFIPE IPLJI JAVBF LAI M43 O9- OCL P2P PQQKQ RIA RIE RNS AAYXX CITATION 7SC 7SP 7TB 8FD FR3 JQ2 KR7 L7M L~C L~D |
| ID | FETCH-LOGICAL-c293t-ccd782d6ed9b1d0497e85d5792486235f53d4c3ae98e096b5e5643d77f19e6f73 |
| IEDL.DBID | RIE |
| ISICitedReferencesCount | 23 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000728138100004&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1556-6013 |
| IngestDate | Sun Nov 30 05:06:09 EST 2025 Sat Nov 29 03:49:42 EST 2025 Tue Nov 18 21:25:32 EST 2025 Wed Aug 27 05:11:52 EDT 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Language | English |
| License | https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html https://doi.org/10.15223/policy-029 https://doi.org/10.15223/policy-037 |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c293t-ccd782d6ed9b1d0497e85d5792486235f53d4c3ae98e096b5e5643d77f19e6f73 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ORCID | 0000-0003-0979-8613 0000-0002-7625-5689 0000-0002-2485-8365 |
| PQID | 2607877506 |
| PQPubID | 85506 |
| PageCount | 16 |
| ParticipantIDs | ieee_primary_9617617 crossref_primary_10_1109_TIFS_2021_3128817 proquest_journals_2607877506 crossref_citationtrail_10_1109_TIFS_2021_3128817 |
| PublicationCentury | 2000 |
| PublicationDate | 20210000 2021-00-00 20210101 |
| PublicationDateYYYYMMDD | 2021-01-01 |
| PublicationDate_xml | – year: 2021 text: 20210000 |
| PublicationDecade | 2020 |
| PublicationPlace | New York |
| PublicationPlace_xml | – name: New York |
| PublicationTitle | IEEE transactions on information forensics and security |
| PublicationTitleAbbrev | TIFS |
| PublicationYear | 2021 |
| Publisher | IEEE The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher_xml | – name: IEEE – name: The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| References | ref57 ref13 ref56 ref12 ref15 ref14 ref52 ref55 ref11 ref10 ref17 ref16 hsu (ref35) 2012; 21 huang (ref58) 2007 ref19 ref18 davis (ref48) 2007 milan (ref50) 2016 ref51 ref46 ref45 ref47 ref42 ref41 ref43 ref8 ref7 ref9 ref4 ref3 ref6 ref5 (ref53) 2021 ref40 cheng (ref31) 2021; 18 ref34 ref37 ref36 ref30 ref33 ref2 ref1 ref38 ester (ref32) 1996; 96 (ref54) 2020 hao (ref63) 2019 simonyan (ref59) 2014 welch (ref44) 1995 ref24 ref23 ref26 ref25 fisher (ref49) 2004 ref20 zeng (ref22) 2010 ref21 ref28 ref27 (ref39) 2003 ref29 ref60 ref62 ref61 |
| References_xml | – year: 2003 ident: ref39 publication-title: Advanced video coding for generic audiovisual services – ident: ref25 doi: 10.1007/s11042-016-3578-9 – volume: 21 start-page: 4593 year: 2012 ident: ref35 article-title: Image feature extraction in encrypted domain with privacy-preserving SIFT publication-title: IEEE Trans Image Process doi: 10.1109/TIP.2012.2204272 – ident: ref46 doi: 10.1016/j.cviu.2016.08.005 – volume: 18 start-page: 1456 year: 2021 ident: ref31 article-title: Person re-identification over encrypted outsourced surveillance videos publication-title: IEEE Trans Dependable Secure Comput – ident: ref20 doi: 10.1109/TCSVT.2016.2645616 – ident: ref3 doi: 10.1145/2633600 – ident: ref7 doi: 10.1109/TIP.2016.2568460 – ident: ref19 doi: 10.1109/TCSVT.2013.2255416 – ident: ref9 doi: 10.1109/TCSVT.2017.2742023 – year: 2019 ident: ref63 article-title: A utility-preserving GAN for face obscuration publication-title: arXiv 1906 11979 – ident: ref8 doi: 10.1145/2886777 – ident: ref57 doi: 10.1109/ICIP.2014.7025068 – ident: ref36 doi: 10.1109/INFOCOM.2017.8056953 – ident: ref27 doi: 10.1109/TIFS.2014.2302899 – year: 2020 ident: ref54 publication-title: Derf's Test Media Collection – year: 2021 ident: ref53 publication-title: Block Ciphers – year: 2007 ident: ref48 publication-title: OTCBVS Benchmark Dataset Collection – ident: ref11 doi: 10.1109/TDSC.2019.2913422 – volume: 96 start-page: 226 year: 1996 ident: ref32 article-title: A density-based algorithm for discovering clusters in large spatial databases with noise publication-title: Proc KDD – year: 1995 ident: ref44 article-title: An introduction to the Kalman filter – ident: ref47 doi: 10.1109/CVPRW.2012.6238919 – ident: ref16 doi: 10.1109/TPAMI.2016.2577031 – ident: ref4 doi: 10.1145/2502081.2502105 – ident: ref5 doi: 10.1007/978-3-642-03168-7_14 – ident: ref10 doi: 10.1109/TCSVT.2015.2433194 – ident: ref1 doi: 10.1109/MSP.2012.2219653 – ident: ref6 doi: 10.1109/TIFS.2013.2262273 – ident: ref45 doi: 10.1109/ICINFA.2010.5512258 – year: 2007 ident: ref58 article-title: Labeled faces in the wild: A database for studying face recognition in unconstrained environments – ident: ref34 doi: 10.1109/TMM.2017.2777470 – start-page: 86 year: 2010 ident: ref22 article-title: Object detection in encryption-based surveillance system publication-title: Proc APSIPA Annu Summit Conf – ident: ref60 doi: 10.1109/CVPR.2016.90 – ident: ref23 doi: 10.1145/2502081.2502157 – ident: ref15 doi: 10.1109/TIP.2014.2378053 – ident: ref37 doi: 10.1109/TPDS.2017.2712148 – ident: ref40 doi: 10.1016/j.jvcir.2009.05.001 – ident: ref41 doi: 10.1109/TMM.2012.2187777 – ident: ref43 doi: 10.1016/j.jvcir.2006.03.004 – ident: ref38 doi: 10.1109/TIFS.2016.2590944 – ident: ref52 doi: 10.1109/CVPR.2011.5995586 – ident: ref29 doi: 10.1109/TMM.2013.2281029 – ident: ref51 doi: 10.1109/CVPR.2013.312 – ident: ref2 doi: 10.1007/3-540-48910-X_16 – ident: ref14 doi: 10.1109/CVPRW.2012.6238922 – ident: ref18 doi: 10.1109/TIP.2012.2214049 – ident: ref30 doi: 10.1109/TCSVT.2019.2929855 – ident: ref17 doi: 10.1109/CVPR.2016.91 – ident: ref33 doi: 10.1109/TCSVT.2011.2129090 – ident: ref55 doi: 10.1109/TCSVT.2019.2924910 – ident: ref24 doi: 10.1007/978-3-319-27671-7_47 – year: 2016 ident: ref50 article-title: MOT16: A benchmark for multi-object tracking publication-title: arXiv 1603 00831 [cs] – ident: ref21 doi: 10.1109/ICCV.2009.5459370 – ident: ref12 doi: 10.1109/34.120329 – ident: ref26 doi: 10.1145/3131342 – year: 2004 ident: ref49 publication-title: CAVIAR Test Case Scenarios – ident: ref62 doi: 10.3390/e20010060 – ident: ref13 doi: 10.1109/CVPR.1999.784637 – ident: ref61 doi: 10.1007/11767831_15 – ident: ref56 doi: 10.1109/FG47880.2020.00021 – ident: ref42 doi: 10.1007/s13369-013-0887-4 – year: 2014 ident: ref59 article-title: Very deep convolutional networks for large-scale image recognition publication-title: arXiv 1409 1556 – ident: ref28 doi: 10.1109/ICIP.2018.8451279 |
| SSID | ssj0044168 |
| Score | 2.4543755 |
| Snippet | Video privacy leakage is becoming an increasingly severe public problem, especially in cloud-based video surveillance systems. It leads to the new need for... |
| SourceID | proquest crossref ieee |
| SourceType | Aggregation Database Enrichment Source Index Database Publisher |
| StartPage | 5381 |
| SubjectTerms | Adaptive algorithms boldsymbol Encrypted video processing Cloud computing Clustering Codec compressed-domain feature Cryptography Domains Encryption Feature extraction Kalman filters Motion detection Motion perception Moving object recognition Multiple target tracking object tracking Privacy Robustness Segmentation Servers Streaming media Surveillance Surveillance systems Vibration Video compression video surveillance |
| Title | Robust Privacy-Preserving Motion Detection and Object Tracking in Encrypted Streaming Video |
| URI | https://ieeexplore.ieee.org/document/9617617 https://www.proquest.com/docview/2607877506 |
| Volume | 16 |
| WOSCitedRecordID | wos000728138100004&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVIEE databaseName: IEEE Electronic Library (IEL) customDbUrl: eissn: 1556-6021 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0044168 issn: 1556-6013 databaseCode: RIE dateStart: 20060101 isFulltext: true titleUrlDefault: https://ieeexplore.ieee.org/ providerName: IEEE |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1Bb9MwFH7qKg5woLAx0a0gHzghzOrEjpPjBKvgQKlgoEkcoth-mSpBUrVppf77PTtJQQJNQsohiuwo8he_975n-30Ar7BMnK9TxqPYIJdYGm50kfJUSVNG2gqpbBCb0PN5enOTLQbw5nAWBhHD5jN862_DWr6r7danyi4ycrd0HcGR1kl7Vqu3uuTV22NvSiWcSEbcrWCKaXZx_XH2lZhgJIigRmkatMl--6AgqvKXJQ7uZTb6vw97Ao-7MJJdtrg_hQFWxzDqJRpYN2OP4dEf9QZP4MeX2mw3DVusl7vC7rnff-FtRXXLPgU1H_Yem7A3q2JF5dhn47M0jPyZ9Rl1tqzYVWXX-xWFqcyvZxe__OPvS4f1M_g2u7p-94F36grckotvuLWOogOXoMuMcEQUNKbKKU2EjFhOrEoVO2njArMUiecYhYqiF6d1KTJMSh2fwrCqK3wOTBorI6kzYaWRGCVGoJiirzMjnIhsOYZpP9657UqPewWMn3mgINMs9xDlHqK8g2gMrw9dVm3djfsan3hMDg07OMYw6UHNu5m5yYm_kY2iOCk5-3evc3jo392mWSYwbNZbfAEP7K5ZbtYvw093B95k1CU |
| linkProvider | IEEE |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3fS9xAEB6sFWofav1RvNbWffCpuJpNdrPJY2k9FPUqehXBh5DdnZQDzcldTvC_7-wmdxVaCoU8hLBLwn7Zmflmd-cD2MMqdb5OGY8Tg1xiZbjRZcYzJU0VayukskFsQg8G2c1NfrEE-4uzMIgYNp_hgb8Na_lubGc-VXaYk7ul6wW8VFLGUXtaa253ya-3B9-USjnRjKRbwxRRfjg86V8RF4wFUdQ4y4I62W8vFGRV_rDFwcH01_7v097Cmy6QZF9a5NdhCesNWJuLNLBuzm7A62cVBzfh9nJsZtOGXUxGj6V94n4HhrcW9U92HvR82Ddswu6smpW1Y9-Nz9Mw8mjW59TZqGZHtZ08PVCgyvyKdnnvH1-PHI634Ef_aPj1mHf6CtySk2-4tY7iA5eiy41wRBU0ZsopTZSMeE6iKpU4aZMS8wyJ6RiFiuIXp3UlckwrnbyD5Xpc4zYwaayMpc6FlUZinBqBIkJfaUY4EduqB9F8vAvbFR_3Ghh3RSAhUV54iAoPUdFB1IPPiy4PbeWNfzXe9JgsGnZw9GBnDmrRzc1pQQyOrBRFSun7v_fahVfHw_Oz4uxkcPoBVv172qTLDiw3kxl-hBX72Iymk0_hB_wFEK3XbA |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Robust+Privacy-Preserving+Motion+Detection+and+Object+Tracking+in+Encrypted+Streaming+Video&rft.jtitle=IEEE+transactions+on+information+forensics+and+security&rft.au=Tian%2C+Xianhao&rft.au=Zheng%2C+Peijia&rft.au=Huang%2C+Jiwu&rft.date=2021&rft.issn=1556-6013&rft.eissn=1556-6021&rft.volume=16&rft.spage=5381&rft.epage=5396&rft_id=info:doi/10.1109%2FTIFS.2021.3128817&rft.externalDBID=n%2Fa&rft.externalDocID=10_1109_TIFS_2021_3128817 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1556-6013&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1556-6013&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1556-6013&client=summon |