Počet záznamů: 1  

Hierarchical Motion Tracking Using Matching of Sparse Features

  1. 1.
    0497814 - ÚI 2019 RIV US eng C - Konferenční příspěvek (zahraniční konf.)
    Pulc, Petr - Holeňa, Martin
    Hierarchical Motion Tracking Using Matching of Sparse Features.
    SITIS 2018. Proceedings of the 14th International Conference on Signal-Image Technology & Internet-Based Systems. Los Alamitos: IEEE Computer Society CPS, 2018 - (Sanniti di Baja, G.; Gallo, L.; Yetongnon, K.; Dipanda, A.; Castrillón-Santana, M.; Chbeir, R.), s. 449-456. ISBN 978-1-5386-9385-8.
    [SITIS 2018. International Conference on Signal Image Technology & Internet Based Systems /14./. Las Palmas de Gran Canaria (ES), 26.11.2018-29.11.2018]
    Grant CEP: GA ČR(CZ) GA18-18080S
    Grant ostatní: ČVUT(CZ) SGS17/210/OHK3/3T/18
    Institucionální podpora: RVO:67985807
    Klíčová slova: motion tracking * image feature detection * image feature description * GPU * CUDA * ORB * Ultra HD
    Obor OECD: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

    Fundamental approaches in motion tracking are based on registration of pixel patches from one frame to another. To ensure invariance to some changes in the image and improve the speed of discovering a match, a pyramidal approach is used to steer the process faster to optima. However, registration of the patches in high resolution is still computationally expensive. Because we require the algorithm to process Ultra HD video content in real time on commonly available hardware, especially on mid-tier graphics processing units, approaches using matching of pixel patches are not feasible. In this paper, we present and evaluate an approach inspired by motion tracking on an image pyramid. However, instead of comparing pixel patches one to another, we utilise binary image descriptors that are much shorter and inherently use a Hamming distance for their direct comparison. Evaluation of our implementation, which is available on GitHub, was carried out on the Multiple Object Tracking challenge dataset.
    Trvalý link: http://hdl.handle.net/11104/0290299

     
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Počet záznamů: 1  

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