dc.contributor.authorGuan, Mingyang
dc.contributor.authorWen, Changyun
dc.contributor.authorShan, Mao
dc.contributor.authorNg, Cheng-Leong
dc.contributor.authorZou, Ying
dc.date.accessioned2018-05-11T08:57:11Z
dc.date.available2018-05-11T08:57:11Z
dc.date.copyright2018
dc.date.issued2018
dc.identifier.citationGuan, M., Wen, C., Shan, M., Ng, C.-L., & Zou, Y. (2018). Real-Time Event-Triggered Object Tracking in the Presence of Model Drift and Occlusion. IEEE Transactions on Industrial Electronics, in press.en_US
dc.identifier.issn0278-0046en_US
dc.identifier.urihttp://hdl.handle.net/10220/44782
dc.description.abstractIn the paper, we propose a novel event-triggered tracking framework for fast and robust visual tracking in the presence of model drift and occlusion. The resulting tracker not only operates at real-time, but also is resilient to tracking failures caused by factors such as heavy occlusion. Specifically, the tracker consists of an event-triggered decision model as the core module that coordinates other functional modules, including a short-term tracker, occlusion and drift identification, target re-detection, short-term tracker updating and on-line discriminative learning for detector. Each functional module is associated with a defined event that is triggered when a set of proposed conditions are met. The occlusion and drift identification module is intended to perform on-line evaluation of the short-term tracking. When a model drift event occurs, the target re-detection module is activated by the event-triggered decision model to relocate the target and reinitialize the short-term tracker. The short-term tracker updating is carried out at each frame with a variable learning rate depending on the degree of occlusion. A sampling-pool is constructed to store discriminative samples that are used to update the detector model. Extensive experiments on large benchmark datasets demonstrate that ETT can effectively detect model drift and restore tracking.en_US
dc.format.extent11 p.en_US
dc.language.isoenen_US
dc.relation.ispartofseriesIEEE Transactions on Industrial Electronicsen_US
dc.rights© 2018 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. The published version is available at: [http://dx.doi.org/10.1109/TIE.2018.2835390].en_US
dc.subjectTarget Trackingen_US
dc.subjectCorrelationen_US
dc.titleReal-Time Event-Triggered Object Tracking in the Presence of Model Drift and Occlusionen_US
dc.typeJournal Article
dc.contributor.researchST Engineering-NTU Corporate Laben_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.identifier.doihttp://dx.doi.org/10.1109/TIE.2018.2835390
dc.description.versionAccepted versionen_US
dc.identifier.rims207568


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