Bayesian modeling of dynamic scenes for object detection

    Authors

    Y. Sheikh;M. Shah

    Comments

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    Abbreviated Journal Title

    IEEE Trans. Pattern Anal. Mach. Intell.

    Keywords

    object detection; kernel density estimation; joint domain range; MAP-MRF; estimation; Computer Science, Artificial Intelligence; Engineering, Electrical &; Electronic

    Abstract

    Accurate detection of moving objects is an important precursor to stable tracking or recognition. In this paper, we present an object detection scheme that has three innovations over existing approaches. First, the model of the intensities of image pixels as independent random variables is challenged and it is asserted that useful correlation exists in intensities of spatially proximal pixels. This correlation is exploited to sustain high levels of detection accuracy in the presence of dynamic backgrounds. By using a nonparametric density estimation method over a joint domain-range representation of image pixels, multimodal spatial uncertainties and complex dependencies between the domain (location) and range (color) are directly modeled. We propose a model of the background as a single probability density. Second, temporal persistence is proposed as a detection criterion. Unlike previous approaches to object detection which detect objects by building adaptive models of the background, the foreground is modeled to augment the detection of objects (without explicit tracking) since objects detected in the preceding frame contain substantial evidence for detection in the current frame. Finally, the background and foreground models are used competitively in a MAP-MRF decision framework, stressing spatial context as a condition of detecting interesting objects and the posterior function is maximized efficiently by finding the minimum cut of a capacitated graph. Experimental validation of the proposed method is performed and presented on a diverse set of dynamic scenes.

    Journal Title

    Ieee Transactions on Pattern Analysis and Machine Intelligence

    Volume

    27

    Issue/Number

    11

    Publication Date

    1-1-2005

    Document Type

    Article

    Language

    English

    First Page

    1778

    Last Page

    1792

    WOS Identifier

    WOS:000231826300008

    ISSN

    0162-8828

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