Adaptive background modeling/subtraction techniques are popular, in particular, because they are able to cope with background variations that are due to lighting variations. Unfortunately these models also tend to adapt to foreground objects that become stationary for a period of time; as a result such objects are no longer considered for further processing. In this paper, we propose the first (to our knowledge) statistically consistent method for incorporating feedback from high-level motion model to modify adaptation behavior. Our approach is based on formulating the background maintenance problem as inference in a continuous state Hidden Markov Model, and combining it with a similarly formulated object tracker in a multichain graphical model framework. We demonstrate that the approximate filtering algorithm in such a framework outperforms the common feed-forward system while not imposing a significant extra computational burden.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Incorporating Object Tracking Feedback into Background Maintenance Framework


    Contributors:


    Publication date :

    2005-01-01


    Size :

    1279309 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English





    Incorporating Priority Preferences into Pavement Maintenance Programming

    Farhan, J. / Fwa, T.F. | British Library Online Contents | 2012



    Incorporating Onboard Diagnostics into Fleet Preventive Maintenance Practices

    Ramani, Tara / Kader, Michael / Johnson, Jeremy et al. | Transportation Research Record | 2015