Speed and texture: An empirical study on optical-flow accuracy in ADAS scenarios

Naveen Onkarappa, Angel Domingo Sappa

    Research output: Contribution to journalArticleResearchpeer-review

    11 Citations (Scopus)


    Increasing mobility in everyday life has led to the concern for the safety of automotives and human life. Computer vision has become a valuable tool for developing driver assistance applications that target such a concern. Many such vision-based assisting systems rely on motion estimation, where optical flow has shown its potential. A variational formulation of optical flow that achieves a dense flow field involves a data term and regularization terms. Depending on the image sequence, the regularization has to appropriately be weighted for better accuracy of the flow field. Because a vehicle can be driven in different kinds of environments, roads, and speeds, optical-flow estimation has to be accurately computed in all such scenarios. In this paper, we first present the polar representation of optical flow, which is quite suitable for driving scenarios due to the possibility that it offers to independently update regularization factors in different directional components. Then, we study the influence of vehicle speed and scene texture on optical-flow accuracy. Furthermore, we analyze the relationships of these specific characteristics on a driving scenario (vehicle speed and road texture) with the regularization weights in optical flow for better accuracy. As required by the work in this paper, we have generated several synthetic sequences along with ground-truth flow fields. © 2013 IEEE.
    Original languageEnglish
    Article number6578171
    Pages (from-to)136-147
    JournalIEEE Transactions on Intelligent Transportation Systems
    Issue number1
    Publication statusPublished - 1 Feb 2014


    • Advanced driver assistance systems (ADASs)
    • optical flow
    • regularization parameters
    • road texture
    • vehicle speed

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