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dc.contributor.authorDuan, Ran
dc.contributor.authorFu, Changhong
dc.contributor.authorAlexis, Konstantinos
dc.contributor.authorKayacan, Erdal
dc.date.accessioned2022-02-15T12:55:29Z
dc.date.available2022-02-15T12:55:29Z
dc.date.created2021-11-25T10:43:32Z
dc.date.issued2021
dc.identifier.isbn978-1-7281-9077-8
dc.identifier.urihttps://hdl.handle.net/11250/2979108
dc.description.abstractIn this paper, we develop an online learning-based visual tracking framework that can optimize the target model and estimate the scale variation for object tracking. We propose a recommender-based tracker, which is capable of selecting the representative convolutional neural network (CNN) layers and feature maps autonomously. In addition, the proposed recommender computes the weights of these layers and feature maps. A discriminative target percept of each recommended layer is reconstructed by the weighted sum of the recommended feature maps. Then the target model of the correlation filter is updated by the weighted sum of the target percepts. Thus, a sub-network is extracted from the pre-trained CNN backbone for the tracking process of a specific target. To deal with scale changes, we propose a spatiotemporal-based min-channel method to estimate the target size variation directly from CNN features. Experimental results on 50 benchmark datasets and video data from a rescue drone demonstrate that the proposed tracker is quite competitive with the state-of-the-art CNN-based trackers in terms of accuracy, scale adaptation, and robustness for UAV-related applications.en_US
dc.language.isoengen_US
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en_US
dc.relation.ispartof2021 IEEE International Conference on Robotics and Automation (ICRA)
dc.relation.urihttps://ieeexplore.ieee.org/document/9562065
dc.titleOnline Recommendation-based Convolutional Features for Scale-Aware Visual Trackingen_US
dc.typeChapteren_US
dc.description.versionacceptedVersionen_US
dc.rights.holder© 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.en_US
dc.source.pagenumber14206-14212en_US
dc.identifier.doi10.1109/ICRA48506.2021.9562065
dc.identifier.cristin1958877
cristin.ispublishedtrue
cristin.fulltextpostprint
cristin.qualitycode1


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