Abstract This paper presents a scalable solution to the problem of tracking objects across spatially separated, uncalibrated, non-overlapping cameras. Unlike other approaches this technique uses an incremental learning method, to model both the colour variations and posterior probability distributions of spatio-temporal links between cameras. These operate in parallel and are then used with an appearance model of the object to track across spatially separated cameras. The approach requires no pre-calibration or batch preprocessing, is completely unsupervised, and becomes more accurate over time as evidence is accumulated.


    Zugriff

    Download


    Exportieren, teilen und zitieren



    Titel :

    Tracking Objects Across Cameras by Incrementally Learning Inter-camera Colour Calibration and Patterns of Activity


    Beteiligte:
    Gilbert, Andrew (Autor:in) / Bowden, Richard (Autor:in)


    Erscheinungsdatum :

    2006-01-01


    Format / Umfang :

    12 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





    Continuously Tracking Objects Across Multiple Widely Separated Cameras

    Cai, Yinghao / Chen, Wei / Huang, Kaiqi et al. | Springer Verlag | 2007


    Persistent Objects Tracking Across Multiple Non Overlapping Cameras

    Kang, Jinman / Cohen, Isaac / Medioni, Gerard | IEEE | 2005


    Learn to Detect Objects Incrementally

    Guan, Linting / Wu, Yan / Zhao, Junqiao et al. | IEEE | 2018


    Incremental, scalable tracking of objects inter camera

    Gilbert, A. / Bowden, R. | British Library Online Contents | 2008