Probabilistic Modeling of Scene Dynamics for Applications in Visual Surveillance

    Authors

    I. Saleemi; K. Shafique;M. Shah

    Abbreviated Journal Title

    IEEE Trans. Pattern Anal. Mach. Intell.

    Keywords

    Vision and scene understanding; Markov processes; machine learning; tracking; kernel density estimation; Metropolis-Hastings; Markov Chain; Monte Carlo; FAST GAUSS TRANSFORM; TRACKING; PATTERNS; Computer Science, Artificial Intelligence; Engineering, Electrical &; Electronic

    Abstract

    We propose a novel method to model and learn the scene activity, observed by a static camera. The proposed model is very general and can be applied for solution of a variety of problems. The motion patterns of objects in the scene are modeled in the form of a multivariate nonparametric probability density function of spatiotemporal variables ( object locations and transition times between them). Kernel Density Estimation is used to learn this model in a completely unsupervised fashion. Learning is accomplished by observing the trajectories of objects by a static camera over extended periods of time. It encodes the probabilistic nature of the behavior of moving objects in the scene and is useful for activity analysis applications, such as persistent tracking and anomalous motion detection. In addition, the model also captures salient scene features, such as the areas of occlusion and most likely paths. Once the model is learned, we use a unified Markov Chain Monte Carlo (MCMC)-based framework for generating the most likely paths in the scene, improving foreground detection, persistent labeling of objects during tracking, and deciding whether a given trajectory represents an anomaly to the observed motion patterns. Experiments with real-world videos are reported which validate the proposed approach.

    Journal Title

    Ieee Transactions on Pattern Analysis and Machine Intelligence

    Volume

    31

    Issue/Number

    8

    Publication Date

    1-1-2009

    Document Type

    Article

    Language

    English

    First Page

    1472

    Last Page

    1485

    WOS Identifier

    WOS:000267050600010

    ISSN

    0162-8828

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