Publication
Title
Occlusion detection and drift-avoidance framework for 2D visual object tracking
Author
Abstract
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.
Language
English
Source (journal)
Signal processing: image communications. - Amsterdam
Publication
Amsterdam : 2021
ISSN
0923-5965
DOI
10.1016/J.IMAGE.2020.116011
Volume/pages
90 (2021) , p. 1-10
Article Reference
116011
ISI
000595038700010
Full text (Publisher's DOI)
UAntwerpen
Publication type
Subject
External links
Web of Science
Record
Identifier
Creation 17.10.2023
Last edited 12.02.2024
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