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Detection of moving foreground objects in videos with strong camera motion

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Abstract

In this paper, we propose a novel method for moving foreground object extraction in sequences taken by a wearable camera, with strong motion. We use camera motion compensated frame differencing, enhanced with a novel kernel-based estimation of the probability density function of background pixels. The probability density functions are used for filtering false foreground pixels on the motion compensated difference frame. The estimation is based on a limited number of measurements; therefore, we introduce a special, spatio-temporal sample point selection and an adaptive thresholding method to deal with this challenge. Foreground objects are built with the DBSCAN algorithm from detected foreground pixels.

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Acknowledgments

This work has been supported by French national grant “Eiffel doctorate” and research project PEPS S2TI CNRS “Wearable video monitoring: application to surveillance of persons with age dementia”, 2007–2008 and BQR grant of University Bordeaux 1. We also thank Pr. Tamás Szirányi, PPCU, Budapest for fruitful discussion when preparing this paper.

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Authors and Affiliations

  1. LABRI, UMR 5800 CNRS, University of Bordeaux, Bordeaux, France

    D. Szolgay

  2. Péter Pázmány Catholic University, Budapest, Hungary

    D. Szolgay

  3. LABRI, UMR 5800 CNRS, University of Bordeaux, Bordeaux, France

    J. Benois-Pineau

  4. IMS, UMR 5218 CNRS, University of Bordeaux, Bordeaux, France

    R. Megret

  5. ISPED, INSERM U 897, University of Bordeaux, Bordeaux, France

    Y. Gaestel & J.-F. Dartigues

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  1. D. Szolgay
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  2. J. Benois-Pineau
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  3. R. Megret
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  4. Y. Gaestel
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  5. J.-F. Dartigues
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Correspondence to D. Szolgay.

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Szolgay, D., Benois-Pineau, J., Megret, R. et al. Detection of moving foreground objects in videos with strong camera motion. Pattern Anal Applic 14, 311–328 (2011). https://doi.org/10.1007/s10044-011-0221-2

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  • DOI: https://doi.org/10.1007/s10044-011-0221-2

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