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    Vision-Based Real-Time Aerial Object Localization and Tracking for UAV Sensing System

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    WangG_2017.pdf (2.816Mb)
    Issue Date
    2017-10-23
    Author
    Wu, Yuanwei
    Sui, Yao
    Wang, Guanghui
    Publisher
    Institute of Electrical and Electronics Engineers
    Type
    Article
    Article Version
    Scholarly/refereed, publisher version
    Rights
    © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, 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 components of this work in other works.
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    Abstract
    This paper focuses on the problem of vision-based obstacle detection and tracking for unmanned aerial vehicle navigation. A real-time object localization and tracking strategy from monocular image sequences is developed by effectively integrating the object detection and tracking into a dynamic Kalman model. At the detection stage, the object of interest is automatically detected and localized from a saliency map computed via the image background connectivity cue at each frame; at the tracking stage, a Kalman filter is employed to provide a coarse prediction of the object state, which is further refined via a local detector incorporating the saliency map and the temporal information between two consecutive frames. Compared with existing methods, the proposed approach does not require any manual initialization for tracking, runs much faster than the state-of-the-art trackers of its kind, and achieves competitive tracking performance on a large number of image sequences. Extensive experiments demonstrate the effectiveness and superior performance of the proposed approach.
    URI
    http://hdl.handle.net/1808/27390
    DOI
    https://doi.org/10.1109/ACCESS.2017.2764419
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    • Electrical Engineering and Computer Science Scholarly Works [301]
    Citation
    Y. Wu, Y. Sui and G. Wang, "Vision-Based Real-Time Aerial Object Localization and Tracking for UAV Sensing System," in IEEE Access, vol. 5, pp. 23969-23978, 2017. doi: 10.1109/ACCESS.2017.2764419

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    Contact KU ScholarWorks
    785-864-8983
    KU Libraries
    1425 Jayhawk Blvd
    Lawrence, KS 66045
    785-864-8983

    KU Libraries
    1425 Jayhawk Blvd
    Lawrence, KS 66045
    Image Credits
     

     

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