In recent years there has been an increasing demand of systems that automatically manage and control the state of large critical areas, such as airports, harbors, parking lots, etc. The framework of the Bayesian Factor Graphs to target fusion seems to be quite promising with respect to classical approaches because of its modularity and because it can naturally integrate very heterogeneous sources of information. The system presented in this paper fuses real-time data coming from various sensors, along with estimates coming from the tracked object models (if available). All the information is merged within environmental constraints in order to provide the best estimate of the state of a moving object. Factor graphs allow the information to flow bidirectionally, to predict the future, or to strengthen our knowledge of the past. In this paper we focus on camera sensors, deployed along the area of interest. The information is merged into the factor graph after geometric inversion and covariance estimate. The problem of automatic localization of moving objects on the images is also addressed. The framework has been tested on a parking area, where states are estimated, accuracy is assessed and considerations about the framework are provided.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Image fusion for object tracking using Factor Graphs


    Contributors:


    Publication date :

    2014-03-01


    Size :

    1387646 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Target tracking using factor graphs and multi-camera systems

    Castaldo, Francesco / Palmieri, Francesco A. N. | IEEE | 2015


    REALTIME PROACTIVE OBJECT FUSION FOR OBJECT TRACKING

    ADAM PAUL A / FELDMAN DMITRIY / CHOI GABRIEL T et al. | European Patent Office | 2021

    Free access


    Object tracking using sensor fusion within a probabilistic framework

    LIU DONGRAN / KADETOTAD SNEHA / CASTRO MARCOS PAUL GERARDO et al. | European Patent Office | 2018

    Free access

    OBJECT TRACKING USING SENSOR FUSION WITHIN A PROBABILISTIC FRAMEWORK

    LIU DONGRAN / KADETOTAD SNEHA / GERARDO CASTRO MARCOS PAUL et al. | European Patent Office | 2018

    Free access