A novel visual tracking algorithm is proposed in this paper. The algorithm plays an important role in a cooperative driving support system (DSSS) that is aimed at reducing traffic fatalities and injuries. The input to the algorithm is a gray-scale image for every video frame from a roadside camera, and the algorithm can be used to detect the existence of vehicles on the road and then track their trajectories. In this algorithm, discriminative pixel-pair feature selection is adopted to discriminate between an image patch with an object in the correct position and image patches with objects in an incorrect position. The proposed algorithm showed stable and precise tracking performance when implemented in various illumination conditions and traffic conditions; the performance was especially good for the low-contrast vehicles running against a high-contrast background.


    Zugriff

    Zugriff über TIB

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Online selection of discriminative pixel-pair feature for tracking


    Weitere Titelangaben:

    Online-Selektion diskriminativer Pixel-Paar-Merkmale für die Objektverfolgung


    Beteiligte:


    Erscheinungsdatum :

    2010


    Format / Umfang :

    8 Seiten, 15 Bilder, 17 Quellen



    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Print


    Sprache :

    Englisch





    On-Line Selection of Discriminative Tracking Features

    Collins, R. / Liu, Y. / IEEE | British Library Conference Proceedings | 2003


    Online selecting discriminative tracking features using particle filter

    Jianyu Wang, / Xilin Chen, / Wen Gao, | IEEE | 2005


    Robust object tracking by online Fisher discrimination boosting feature selection

    Yang, Jing / Zhang, Kaihua / Liu, Qingshan | British Library Online Contents | 2016


    Robust object tracking by online Fisher discrimination boosting feature selection

    Yang, Jing / Zhang, Kaihua / Liu, Qingshan | British Library Online Contents | 2016