Camera localization using trajectories and maps

  • Raul Mohedano, Andrea Cavallaro, and Narciso Garcia. Camera localization using trajectories and maps. IEEE Transactions on Pattern Analysis and Machine Intelligence, 36(4):684-697, 2014. doi:10.1109/TPAMI.2013.243
    [BibTeX] [Abstract]

    We propose a new Bayesian framework for automatically determining the position (location and orientation) of an uncalibrated camera using the observations of moving objects and a schematic map of the passable areas of the environment. Our approach takes advantage of static and dynamic information on the scene structures through prior probability distributions for object dynamics. The proposed approach restricts plausible positions where the sensor can be located while taking into account the inherent ambiguity of the given setting. The proposed framework samples from the posterior probability distribution for the camera position via data driven MCMC, guided by an initial geometric analysis that restricts the search space. A Kullback-Leibler divergence analysis is then used that yields the final camera position estimate, while explicitly isolating ambiguous settings. The proposed approach is evaluated in synthetic and real environments, showing its satisfactory performance in both ambiguous and unambiguous settings.

    @Article{2014-04-MOHEDANO,
    title={{Camera localization using trajectories and maps}},
    author={Raul Mohedano and Andrea Cavallaro and Narciso Garcia},
    journal={{IEEE Transactions on Pattern Analysis and Machine Intelligence}},
    volume={36},
    number={4},
    pages={684-697},
    date={2014-04-01},
    year={2014},
    doi={10.1109/TPAMI.2013.243},
    abstract={We propose a new Bayesian framework for automatically determining the position (location and orientation) of an uncalibrated camera using the observations of moving objects and a schematic map of the passable areas of the environment. Our approach takes advantage of static and dynamic information on the scene structures through prior probability distributions for object dynamics. The proposed approach restricts plausible positions where the sensor can be located while taking into account the inherent ambiguity of the given setting. The proposed framework samples from the posterior probability distribution for the camera position via data driven MCMC, guided by an initial geometric analysis that restricts the search space. A Kullback-Leibler divergence analysis is then used that yields the final camera position estimate, while explicitly isolating ambiguous settings. The proposed approach is evaluated in synthetic and real environments, showing its satisfactory performance in both ambiguous and unambiguous settings.}
    }

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