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dc.contributor.authorSheu, Ruey-Kaien_US
dc.contributor.authorPardeshi, Mayureshen_US
dc.contributor.authorChen, Lun-Chien_US
dc.contributor.authorYuan, Shyan-Mingen_US
dc.date.accessioned2019-09-02T07:46:16Z-
dc.date.available2019-09-02T07:46:16Z-
dc.date.issued2019-07-01en_US
dc.identifier.issn1424-8220en_US
dc.identifier.urihttp://dx.doi.org/10.3390/s19133016en_US
dc.identifier.urihttp://hdl.handle.net/11536/152665-
dc.description.abstractThere is strong demand for real-time suspicious tracking across multiple cameras in intelligent video surveillance for public areas, such as universities, airports and factories. Most criminal events show that the nature of suspicious behavior are carried out by un-known people who try to hide themselves as much as possible. Previous learning-based studies collected a large volume data set to train a learning model to detect humans across multiple cameras but failed to recognize newcomers. There are also several feature-based studies aimed to identify humans within-camera tracking. It would be very difficult for those methods to get necessary feature information in multi-camera scenarios and scenes. It is the purpose of this study to design and implement a suspicious tracking mechanism across multiple cameras based on correlation filters, called suspicious tracking across multiple cameras based on correlation filters (STAM-CCF). By leveraging the geographical information of cameras and YOLO object detection framework, STAM-CCF adjusts human identification and prevents errors caused by information loss in case of object occlusion and overlapping for within-camera tracking cases. STAM-CCF also introduces a camera correlation model and a two-stage gait recognition strategy to deal with problems of re-identification across multiple cameras. Experimental results show that the proposed method performs well with highly acceptable accuracy. The evidences also show that the proposed STAM-CCF method can continuously recognize suspicious behavior within-camera tracking and re-identify it successfully across multiple cameras.en_US
dc.language.isoen_USen_US
dc.subjectsuspicious trackingen_US
dc.subjectsurveillanceen_US
dc.subjectmulti-camera trackingen_US
dc.subjectfeature based trackingen_US
dc.titleSTAM-CCF: Suspicious Tracking Across Multiple Camera Based on Correlation Filtersen_US
dc.typeArticleen_US
dc.identifier.doi10.3390/s19133016en_US
dc.identifier.journalSENSORSen_US
dc.citation.volume19en_US
dc.citation.issue13en_US
dc.citation.spage0en_US
dc.citation.epage0en_US
dc.contributor.department交大名義發表zh_TW
dc.contributor.department資訊工程學系zh_TW
dc.contributor.departmentNational Chiao Tung Universityen_US
dc.contributor.departmentDepartment of Computer Scienceen_US
dc.identifier.wosnumberWOS:000477045000174en_US
dc.citation.woscount0en_US
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