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Title
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Occlusion detection and drift-avoidance framework for 2D visual object tracking
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Author
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Abstract
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| his paper presents a long-term 2D tracking framework for the coverage of live outdoor (e.g., sports) events that is suitable for embedded system application (e.g. Unmanned Aerial Vehicles). This application scenario requires 2D target (e.g., athlete, ball, bicycle, boat) tracking for visually assisting the UAV pilot (or cameraman) to maintain proper target framing, or even for actual 3D target following/localization when the drone flies autonomously. In these cases, it should be expected that the target to be tracked/followed, may disappear from the UAV camera field of view, due to fast 3D target motion, illumination changes, or due to visual target occlusions by obstacles, even if the actual UAV continues following it (either autonomously, by exploiting alternative target localization sensors, or by pilot maneuvering). Therefore, the 2D tracker should be able to recover from such situations. The proposed framework solves exactly this problem. Target occlusions are detected from the 2D tracker responses. Depending on the occlusion immensity, the proposed framework decides whether to not update the tracking model, or to employ target re-detection in a broader window. As a result, the proposed framework allows continued target tracking once the target re-appears in the video stream, without tracker re-initialization. |
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Language
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English
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Source (journal)
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Signal processing: image communications. - Amsterdam
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Publication
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Amsterdam
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2021
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ISSN
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0923-5965
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DOI
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10.1016/J.IMAGE.2020.116011
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Volume/pages
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90
(2021)
, p. 1-10
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Article Reference
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116011
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ISI
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000595038700010
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Medium
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E-only publicatie
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Full text (Publisher's DOI)
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